Academic literature on the topic 'User reviews'

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Journal articles on the topic "User reviews"

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Hussain, Jamil, Zahra Azhar, Hafiz Farooq Ahmad, Muhammad Afzal, Mukhlis Raza, and Sungyoung Lee. "User Experience Quantification Model from Online User Reviews." Applied Sciences 12, no. 13 (2022): 6700. http://dx.doi.org/10.3390/app12136700.

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Due to the advancement in information technology and the boom of micro-blogging platforms, a growing number of online reviews are posted daily on product distributed platforms in the form of spontaneous and insightful user feedback, and these can be used as a significant data source to understand user experience (UX) and satisfaction. However, despite the vast amount of online reviews, the existing literature focuses on online ratings and ignores the real textual context in reviews. We proposed a three-step UX quantification model from online reviews to understand customer satisfaction using the effect-based Kano model. First, the relevant online reviews are selected using various filter mechanisms. Second, UX dimensions (UXDs) are extracted using a proposed method called UX word embedding Latent Dirichlet allocation (UXWE-LDA) and sentiment orientation using a transformer-based pipeline. Then, the casual relationships are identified for the extracted UXDs. Third, the UXDs are mapped on the customer satisfaction model (effect-based Kano) to understand the user perspective about the system, product, or services. Finally, the different parts of the proposed quantification model are evaluated to examine the performance of this method. We present different results of the proposed method in terms of accuracy, topic coherence (TC), Topic-wise performance, and expert-based evaluation for the proposed framework validation. For review quality filters, we achieved 98.49% accuracy for the spam detection classifier and 95% accuracy for the relatedness detection classifier. The results show that the proposed method for the topic extractor module always gives a higher TC value than other models such as WE-LDA and LDA. Regarding topic-wise performance measures, UXWE-LDA achieves a 3% improvement on average compared to LDA due to the incorporation of semantic domain knowledge. We also compute the Jaccard coefficient similarity between the extracted dimensions using UXWE-LDA and UX experts-based analysis for checking the mutual agreement, which is 0.3, 0.5, and 0.4, respectively. Based on the Kano model, the presented study has potential implications concerning issues and knowing the product’s strengths and weaknesses in product design.
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Zhou, Wenqi, and Wenjing Duan. "Do Professional Reviews Affect Online User Choices Through User Reviews? An Empirical Study." Journal of Management Information Systems 33, no. 1 (2016): 202–28. http://dx.doi.org/10.1080/07421222.2016.1172460.

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Santiago, Mara Taynar, and Anna Beatriz Marques. "Exploring user reviews to identify accessibility problems in applications for autistic users." Journal on Interactive Systems 14, no. 1 (2023): 317–30. http://dx.doi.org/10.5753/jis.2023.3238.

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The Google Play Store provides various user reviews that can provide information about user experience, usability, and accessibility. Despite multiple studies addressing these reviews’ importance and contributions to improving interactive systems, accessibility for users with Autism Spectrum Disorder (ASD) is still little discussed in this context. Considering the potential of user reviews, this article presents a textual analysis of reviews extracted from eight educational applications available in Portuguese with a focus on autistic children, namely: “ABC Autismo”, “Aprendendo com Biel e seus amigos”, “AutApp Autismo”, “Autismo projeto integrar”, “Jade Autismo”, “Matraquinha”, “OTO (Olhar Tocar Ouvir)” and “Teacch.me”. We conducted an analysis based on the Guidelines for Accessible Interfaces for People with Autism (GAIA) and the BBC Mobile Accessibility Guidelines to classify user reviews.
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J.S, Ravikumar, Narayana Reddy T, and Syed Mohammad Ghouse. "“User Generated Reviews and Business Promotions”." International Journal of Psychosocial Rehabilitation 24, no. 02 (2020): 1619–29. http://dx.doi.org/10.37200/ijpr/v24i2/pr200464.

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Wang, Bingkun, Bing Chen, Li Ma, and Gaiyun Zhou. "User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix." Information 10, no. 1 (2018): 1. http://dx.doi.org/10.3390/info10010001.

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With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained not only by the review text but also by the user-item rating matrix. Therefore, we propose a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information. In our approach, each user has a personalized review rating prediction model, which is decomposed into two components, one part is based on review text and the other is based on user-item rating matrix. Through extensive experiments on Yelp and Douban datasets, we validate that our methods can significantly outperform the state-of-the-art methods.
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Alnusyan, Ruba, Ruba Almotairi, Sarah Almufadhi, Amal A. Al-Shargabi, and Jowharah F. Alshobaili. "Hybrid Approach for User Reviews' Text Analysis and Visualization: A Case Study of Amazon User Reviews." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 08 (2022): 79–93. http://dx.doi.org/10.3991/ijim.v16i08.30169.

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Nowadays, many people prefer to purchase through online websites. Usually, those people start with reading user reviews and comments before making a purchase decision. The user reviews are considered powerful sources of information about products, in which users share opinions and previous experiences on using these products. However, these reviews are mostly textual and uncategorized. Thus, new customers need to read a massive amount of reviews, one by one, to make a decision. This study attempts to bridge this gap and proposes a hybrid approach of topic modeling that combines supervised and unsupervised learning. In particular, the study collected a massive amount of Amazon user reviews, analyzed the reviews' texts, and combined two approaches of topic modeling, which are unsupervised and supervised learning, i.e., semi-supervised learning. Besides, the study makes classification on reviews based on sentiment analysis. The resulting reviews' topics and their sentiment classifications are displayed on a visual dashboard. The proposed hybrid approach showed better performance in terms of text analysis and clearer representation of review topics. The outcome of this study helps customers make their decision on purchase products in a more effortless and clearer way.
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Alnusyan, Ruba, Ruba Almotairi, Sarah Almufadhi, Amal A. Al-Shargabi, and Jowharah F. Alshobaili. "Hybrid Approach for User Reviews' Text Analysis and Visualization: A Case Study of Amazon User Reviews." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 08 (2022): 79–93. http://dx.doi.org/10.3991/ijim.v16i08.30169.

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Nowadays, many people prefer to purchase through online websites. Usually, those people start with reading user reviews and comments before making a purchase decision. The user reviews are considered powerful sources of information about products, in which users share opinions and previous experiences on using these products. However, these reviews are mostly textual and uncategorized. Thus, new customers need to read a massive amount of reviews, one by one, to make a decision. This study attempts to bridge this gap and proposes a hybrid approach of topic modeling that combines supervised and unsupervised learning. In particular, the study collected a massive amount of Amazon user reviews, analyzed the reviews' texts, and combined two approaches of topic modeling, which are unsupervised and supervised learning, i.e., semi-supervised learning. Besides, the study makes classification on reviews based on sentiment analysis. The resulting reviews' topics and their sentiment classifications are displayed on a visual dashboard. The proposed hybrid approach showed better performance in terms of text analysis and clearer representation of review topics. The outcome of this study helps customers make their decision on purchase products in a more effortless and clearer way.
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Mcfarlane, G. W. P. "Book Reviews : User-Friendl y tHeology." Expository Times 110, no. 9 (1999): 303. http://dx.doi.org/10.1177/001452469911000924.

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Carey, Tom. "Video reviews: USER INTERFACE STRATEGIES '88." ACM SIGCHI Bulletin 21, no. 2 (1989): 128–30. http://dx.doi.org/10.1145/70609.1047718.

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Trivedi, Shrawan Kumar, and Shubhamoy Dey. "Analysing user sentiment of Indian movie reviews." Electronic Library 36, no. 4 (2018): 590–606. http://dx.doi.org/10.1108/el-08-2017-0182.

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Purpose To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be achieved via natural language processing and machine learning classifiers. This paper aims to propose a novel probabilistic committee selection classifier (PCC) to analyse and classify the sentiment polarities of movie reviews. Design/methodology/approach An Indian movie review corpus is assembled for this study. Another publicly available movie review polarity corpus is also involved with regard to validating the results. The greedy stepwise search method is used to extract the features/words of the reviews. The performance of the proposed classifier is measured using different metrics, such as F-measure, false positive rate, receiver operating characteristic (ROC) curve and training time. Further, the proposed classifier is compared with other popular machine-learning classifiers, such as Bayesian, Naïve Bayes, Decision Tree (J48), Support Vector Machine and Random Forest. Findings The results of this study show that the proposed classifier is good at predicting the positive or negative polarity of movie reviews. Its performance accuracy and the value of the ROC curve of the PCC is found to be the most suitable of all other classifiers tested in this study. This classifier is also found to be efficient at identifying positive sentiments of reviews, where it gives low false positive rates for both the Indian Movie Review and Review Polarity corpora used in this study. The training time of the proposed classifier is found to be slightly higher than that of Bayesian, Naïve Bayes and J48. Research limitations/implications Only movie review sentiments written in English are considered. In addition, the proposed committee selection classifier is prepared only using the committee of probabilistic classifiers; however, other classifier committees can also be built, tested and compared with the present experiment scenario. Practical implications In this paper, a novel probabilistic approach is proposed and used for classifying movie reviews, and is found to be highly effective in comparison with other state-of-the-art classifiers. This classifier may be tested for different applications and may provide new insights for developers and researchers. Social implications The proposed PCC may be used to classify different product reviews, and hence may be beneficial to organizations to justify users’ reviews about specific products or services. By using authentic positive and negative sentiments of users, the credibility of the specific product, service or event may be enhanced. PCC may also be applied to other applications, such as spam detection, blog mining, news mining and various other data-mining applications. Originality/value The constructed PCC is novel and was tested on Indian movie review data.
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Dissertations / Theses on the topic "User reviews"

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Jagithyala, Anirudh. "Recommending recipes based on ingredients and user reviews." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18154.

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Master of Science<br>Department of Computing and Information Sciences<br>Doina Caragea<br>In recent years, the content volume and number of users of the Web have increased dramatically. This large amount of data has caused an information overload problem, which hinders the ability of a user to find the relevant data at the right time. Therefore, the primary task of recommendation systems is to analyze data in order to offer users suggestions for similar data. Recommendations which use the core content are known as content-based recommendation or content filtering, and recommendations which utilize directly the user feedback are known as collaborative filtering. This thesis presents the design, implementation, testing, and evaluation of a recommender system within the recipe domain, where various approaches for producing recommendations are utilized. More specifically, this thesis discusses approaches derived from basic recommendation algorithms, but customized to take advantage of specific data available in the {\it recipe} domain. The proposed approaches for recommending recipes make use of recipe ingredients and reviews. We first build ingredient vectors for both recipes and users (based on recipes they have rated highly), and recommend new recipes to users based on the similarity between user and recipe ingredient vectors. Similarly, we build recipe and user vectors based on recipe review text, and recommend new recipes based on the similarity between user and recipe review vectors. At last, we study a hybrid approach, where both ingredients and reviews are used together. Our proposed approaches are tested over an existing dataset crawled from recipes.com. Experimental results show that the recipe ingredients are more informative than the review text for making recommendations. Furthermore, when using ingredients and reviews together, the results are better than using just the reviews, but worse than using just the ingredients, suggesting that to make use of reviews, the review vocabulary needs better filtering.
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Wang, Ji. "Clustered Layout Word Cloud for User Generated Online Reviews." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/19193.

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User generated reviews, like those found on Yelp and Amazon, have become important refer- ence material in casual decision making, like dining, shopping and entertainment. However, very large amounts of reviews make the review reading process time consuming. A text visualization can speed up the review reading process.<br /><br />In this thesis, we present the clustered layout word cloud -- a text visualization that quickens decision making based on user generated reviews. We used a natural language processing approach, called grammatical dependency parsing, to analyze user generated review content and create a semantic graph. A force-directed graph layout was applied to the graph to create the clustered layout word cloud.<br /><br />We conducted a two-task user study to compare the clustered layout word cloud to two alternative review reading techniques: random layout word cloud and normal block-text reviews. The results showed that the clustered layout word cloud offers faster task completion time and better user satisfaction than the other two alternative review reading techniques. [Permission email from J. Huang removed at his request. GMc March 11, 2014]<br>Master of Science
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Konstantinova, Natalia. "Knowledge acquisition from user reviews for interactive question answering." Thesis, University of Wolverhampton, 2013. http://hdl.handle.net/2436/297401.

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Nowadays, the effective management of information is extremely important for all spheres of our lives and applications such as search engines and question answering systems help users to find the information that they need. However, even when assisted by these various applications, people sometimes struggle to find what they want. For example, when choosing a product customers can be confused by the need to consider many features before they can reach a decision. Interactive question answering (IQA) systems can help customers in this process, by answering questions about products and initiating a dialogue with the customers when their needs are not clearly defined. The focus of this thesis is how to design an interactive question answering system that will assist users in choosing a product they are looking for, in an optimal way, when a large number of similar products are available. Such an IQA system will be based on selecting a set of characteristics (also referred to as product features in this thesis), that describe the relevant product, and narrowing the search space. We believe that the order in which these characteristics are presented in terms of these IQA sessions is of high importance. Therefore, they need to be ranked in order to have a dialogue which selects the product in an efficient manner. The research question investigated in this thesis is whether product characteristics mentioned in user reviews are important for a person who is likely to purchase a product and can therefore be used when designing an IQA system. We focus our attention on products such as mobile phones; however, the proposed techniques can be adapted for other types of products if the data is available. Methods from natural language processing (NLP) fields such as coreference resolution, relation extraction and opinion mining are combined to produce various rankings of phone features. The research presented in this thesis employs two corpora which contain texts related to mobile phones specifically collected for this thesis: a corpus of Wikipedia articles about mobile phones and a corpus of mobile phone reviews published on the Epinions.com website. Parts of these corpora were manually annotated with coreference relations, mobile phone features and relations between mentions of the phone and its features. The annotation is used to develop a coreference resolution module as well as a machine learning-based relation extractor. Rule-based methods for identification of coreference chains describing the phone are designed and thoroughly evaluated against the annotated gold standard. Machine learning is used to find links between mentions of the phone (identified by coreference resolution) and phone features. It determines whether some phone feature belong to the phone mentioned in the same sentence or not. In order to find the best rankings, this thesis investigates several settings. One of the hypotheses tested here is that the relatively low results of the proposed baseline are caused by noise introduced by sentences which are not directly related to the phone and phone feature. To test this hypothesis, only sentences which contained mentions of the mobile phone and a phone feature linked to it were processed to produce rankings of the phones features. Selection of the relevant sentences is based on the results of coreference resolution and relation extraction. Another hypothesis is that opinionated sentences are a good source for ranking the phone features. In order to investigate this, a sentiment classification system is also employed to distinguish between features mentioned in positive and negative contexts. The detailed evaluation and error analysis of the methods proposed form an important part of this research and ensure that the results provided in this thesis are reliable.
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Zhang, Xuan. "Product Defect Discovery and Summarization from Online User Reviews." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85581.

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Product defects concern various groups of people, such as customers, manufacturers, government officials, etc. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. As a kind of opinion mining research, existing defect discovery methods mainly focus on how to classify the type of product issues, which is not enough for users. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. These challenges cannot be solved by existing aspect-oriented opinion mining models, which seldom consider the defect entities mentioned above. Furthermore, users also want to better capture the semantics of review text, and to summarize product defects more accurately in the form of natural language sentences. However, existing text summarization models including neural networks can hardly generalize to user review summarization due to the lack of labeled data. In this research, we explore topic models and neural network models for product defect discovery and summarization from user reviews. Firstly, a generative Probabilistic Defect Model (PDM) is proposed, which models the generation process of user reviews from key defect entities including product Model, Component, Symptom, and Incident Date. Using the joint topics in these aspects, which are produced by PDM, people can discover defects which are represented by those entities. Secondly, we devise a Product Defect Latent Dirichlet Allocation (PDLDA) model, which describes how negative reviews are generated from defect elements like Component, Symptom, and Resolution. The interdependency between these entities is modeled by PDLDA as well. PDLDA answers not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, the problem of how to summarize user reviews more accurately, and better capture the semantics in them, is studied using deep neural networks, especially Hierarchical Encoder-Decoder Models. For each of the research topics, comprehensive evaluations are conducted to justify the effectiveness and accuracy of the proposed models, on heterogeneous datasets. Further, on the theoretical side, this research contributes to the research stream on product defect discovery, opinion mining, probabilistic graphical models, and deep neural network models. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials.<br>Ph. D.<br>Product defects concern various groups of people, such as customers, manufacturers, and government officials. Thus, defect-related knowledge and information are essential. In keeping with the growth of social media, online forums, and Internet commerce, people post a vast amount of feedback on products, which forms a good source for the automatic acquisition of knowledge about defects. However, considering the vast volume of online reviews, how to automatically identify critical product defects and summarize the related information from the huge number of user reviews is challenging, even when we target only the negative reviews. People expect to see defect information in multiple facets, such as product model, component, and symptom, which are necessary to understand the defects and quantify their influence. In addition, people are eager to seek problem resolutions once they spot defects. Furthermore, users also want to better summarize product defects more accurately in the form of natural language sentences. These requirements cannot be satisfied by existing methods, which seldom consider the defect entities mentioned above, or hardly generalize to user review summarization. In this research, we develop novel Machine Learning (ML) algorithms for product defect discovery and summarization. Firstly, we study how to identify product defects and their related attributes, such as Product Model, Component, Symptom, and Incident Date. Secondly, we devise a novel algorithm, which can discover product defects and the related Component, Symptom, and Resolution, from online user reviews. This method tells not only what the defects look like, but also how to address them using the crowd wisdom hidden in user reviews. Finally, we address the problem of how to summarize user reviews in the form of natural language sentences using a paraphrase-style method. On the theoretical side, this research contributes to multiple research areas in Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning. Regarding impact, these techniques will benefit related users such as customers, manufacturers, and government officials.
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Hassan, Ehab. "Event-Based Recognition Of Lived : Experiences In User Reviews." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCD021/document.

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La quantité de contenu généré par l'utilisateur sur le Web croît à un rythme rapide.Une grande partie de ce contenu est constituée des opinions et avis sur des produits et services. Vu leur impact, ces avis sont un facteur important dans les décisions concernant l'achat de ces produits ou services. Les utilisateurs ont tendance à faire confiance aux autres utilisateurs, surtout s'ils peuvent se comparer à ceux qui ont écrit les avis, ou, en d'autres termes, ils sont confiants de partager certaines caractéristiques. Par exemple, les familles préféreront voyager dans les endroits qui ont été recommandés par d'autres familles. Nous supposons que les avis qui contiennent des expériences vécues sont plus précieuses, puisque les expériences donnent aux avis un aspect plus subjective, permettant aux lecteurs de se projeter dans le contexte de l'écrivain.En prenant en compte cette hypothèse, dans cette thèse, nous visons à identifier, extraire et représenter les expériences vécues rapportées dans les avis des utilisateurs en hybridant les techniques d'extraction des connaissances et de traitement du langage naturel,afin d'accélérer le processus décisionnel. Pour cela, nous avons défini opérationnellement une expérience vécue d'un utilisateur comme un événement mentionné dans un avis, où l'auteur est présent parmi les participants. Cette définition considère que les événements mentionnés dans le texte sont les éléments les plus importants dans les expériences vécues: toutes les expériences vécues sont basées sur des événements, qui sont clairement définis dans le temps et l'espace. Par conséquent, nous proposons une approche permettant d'extraire les événements à partir des avis des utilisateurs, qui constituent la base d'un système permettant d'identifier et extraire les expériences vécues.Pour l'approche d'extraction d'événements, nous avons transformé les avis des utilisateur sen leurs représentations sémantiques en utilisant des techniques de machine reading.Nous avons effectué une analyse sémantique profonde des avis et détecté les cadres linguistiques les plus appropriés capturant des relations complexes exprimées dans les avis. Le système d'extraction des expériences vécues repose sur trois étapes. La première étape opère un filtrage des avis, basé sur les événements, permettant d'identifier les avis qui peuvent contenir des expériences vécues. La deuxième étape consiste à extraire les événements pertinents avec leurs participants. La dernière étape consiste à représenter les expériences vécues extraites de chaque avis comme un sous-graphe d'événements contenant les événements pertinents et leurs participants.Afin de tester notre hypothèse, nous avons effectué quelques expériences pour vérifier si les expériences vécues peuvent être considérées comme des motivations pour les notes attribuées par les utilisateurs dans le système de notation. Par conséquent, nous avons utilisé les expériences vécues comme des caractéristiques dans un système de classification, en comparant avec les notes associées avec des avis dans un ensemble de données extraites et annotées manuellement de Tripadvisor. Les résultats montrent que les expériences vécues sont corrélées avec les notes. Cette thèse fournit des contributions intéressantes dans le domaine de l'analyse d'opinion. Tout d'abord, l'application avec succès de machine reading afin d'identifier les expériences vécues. Ensuite, La confirmation que les expériences vécues sont liées aux notations. Enfin, l'ensemble de données produit pour tester notre hypothèse constitue également une contribution importante de la thèse<br>The quantity of user-generated content on the Web is constantly growing at a fast pace.A great share of this content is made of opinions and reviews on products and services.This electronic word-of-mouth is also an important factor in decisions about purchasing these products or services. Users tend to trust other users, especially if they can compare themselves to those who wrote the reviews, or, in other words, they are confident to share some characteristics. For instance, families will prefer to travel in places that have been recommended by other families. We assume that reviews that contain lived experiences are more valuable, since experiences give to the reviews a more subjective cut, allowing readers to project themselves into the context of the writer. With this hypothesis in mind, in this thesis we aim to identify, extract, and represent reported lived experiences in customer reviews by hybridizing Knowledge Extraction and Natural Language Processing techniques in order to accelerate the decision process. Forthis, we define a lived user experience as an event mentioned in a review, where the authoris among the participants. This definition considers that mentioned events in the text are the most important elements in lived experiences : all lived experiences are based on events,which on turn are clearly defined in time and space. There fore, we propose an approach to extract events from user reviews, which constitute the basis of an event-based system to identify and extract lived experiences. For the event extraction approach, we transform user reviews into their semantic representations using machine reading techniques. We perform a deep semantic parsing of reviews, detecting the linguistic frames that capture complex relations expressed in there views. The event-based lived experience system is carried out in three steps. The first step operates an event-based review filtering, which identifies reviews that may contain lived experiences. The second step consists of extracting relevant events together with their participants. The last step focuses on representing extracted lived experiences in each review as an event sub-graph.In order to test our hypothesis, we carried out some experiments to verify whether lived experiences can be considered as triggers for the ratings expressed by users. Therefore, we used lived experiences as features in a classification system, comparing with the ratings of the reviews in a dataset extracted and manually annotated from Tripadvisor. The results show that lived experiences are actually correlated with the ratings.In conclusion, this thesis provides some interesting contributions in the field of opinionmining. First of all, the successful application of machine reading to identify lived experiences. Second, the confirmation that lived experiences are correlated to ratings. Finally,the dataset produced to test our hypothesis constitutes also an important contribution of the thesis
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Sherwani, Dara. "What makes reviews trustworthy? : an investigation of user trust in online reviews when making purchase decisions." Thesis, City, University of London, 2016. http://openaccess.city.ac.uk/15408/.

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With the growing number of systems that provide user-generated reviews the relationship between users and vendors, particularly unfamiliar vendors, is changing. Users are increasingly using online reviews for assessing vendors’ services prior to purchasing them. However, users might be uncertain how much to trust reviews because most users are unfamiliar with reviewers and reviews might not be credible. Thus, it is becoming increasingly important to understand which reviews are trusted by users when they make purchase decisions and why. Previous work has suggested that factors of the review and reviewer - perceived review valence, quality, helpfulness, accuracy, perceived reviewer’s expertise and bias - influence user trust. It has also suggested that interface signals, such as the total number of reviews posted by the reviewer, are employed by users when deciding to trust reviews and reviewers as part of their purchase decision-making. This research aims to advance knowledge regarding user trust in online reviews when making purchase decisions. It first explores how users employ interface signals in their perception of factors of the review and reviewer that influence trust. Second, it clarifies how these factors relate to one another and to trust. It explores the role of new factors - perceived reviewer’s personality and personality similarity to the user - that have not been previously considered in trust in online reviews. Third, it demonstrates how the user’s own background - dispositional trust, past experience and personality - shapes trust in online reviews. To do so, this research involved three empirical studies, two of which were lab-based studies that collected qualitative and quantitative data and one online study that collected quantitative data. The findings show that there are two categories of interface signals, reviewrelated and reviewer-related that matter in trust. Review-related signals seem more important not only in trust overall, but also are employed by users to perceive factors of both review and reviewer that influence trust more so than reviewer-related signals. Regarding the interplay between the factors that have been suggested to influence trust, it seems that user perception of these factors are related to one another. The perceived quality and helpfulness of the review seem to be most related to the perceived reviewer’s expertise and the perceived review accuracy seems to be most related to perceived reviewer’s bias. While all these factors relate to trust, factors of the review seem to have a more significant role. The findings also show that the perceived reviewer’s personality relates to trust and factors that can influence trust. For instance, the reviewer’s perceived high conscientiousness is related to high perceived review quality, high perceived reviewer’s expertise and high trust. The perceived reviewer’s personality similarity to the user seems to play a weaker role in trust than the perceived reviewer’s personality. The user’s own background seems to have a significant role in shaping trust in online reviews. High dispositional trust, extraversion and neuroticism are related to high perceived review quality, accuracy, high perceived reviewer’s expertise and high trust. The user’s positive past experience of using online reviews is related to high willingness of making a purchase based on reviews. This research makes several theoretical and practical contributions. It builds on previous work on user trust in online reviews and vendors, and the perception of personality. The findings point the way towards a framework of trust relationships in systems that provide user-generated reviews. Also, the findings have design implications because they show which and how interface signals can influence trust.
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Dahlgren, Sara, Amelie Johnson, and Caroline Liljenberg. "Online Reviews - What Motivates You? : A qualitative study of Customers' Motivation to Write Online Reviews." Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-44501.

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Background: To understand the consumers’ motivation to write online reviews is of importance, especially for companies since a large number of reviews have a positive influence on sales. Previous research has been done regarding what motivate consumers to provide user generated content, online word of mouth and also, to some extent, online reviews. However, these studies have primarily been adopted in a quantitative manner. To explore, from customers’ own perspective, the motivation to write online reviews is therefore valuable to add depth to the existing literature. Purpose: The purpose of this study is to explore customers’ motivation to write online reviews. Research question: What factors motivate customers to write online reviews? Methodology: The design of the research is a case study where the data collection method was conducted by semi-structured interviews. Conclusion: The result of this study shows that customers’ motivation to write online reviews is due to a variety of situations. The customers are motivated to write to enhance their selfimage, helping both customers and companies, and in some situations to even harm companies. Also, customers were motivated to write to obtain economical incentives. The features of the platform are important, where easiness and the opportunity to be anonymous were preferred.
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Zifla, Ermira. "Three Essays on Social and Economic Effects of User-Generated Content." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/509966.

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Business Administration/Management Information Systems<br>D.B.A.<br>In this dissertation, I investigate how online social interactions and user-generated content affect sellers and consumers in online platforms. I conduct three empirical studies to understand the effect of user-generated content in three different types of online platforms: (1) an e-commerce marketplace, (2) an online reviews platform, and (3) an online health community. In study one, I examine how social features (e.g., following others, sharing others’ products) within an electronic commerce marketplace affect status and sales for sellers. This essay contributes to the literature on electronic commerce by deepening the understanding of online social processes among sellers. In study two, I explore how humorous appropriation of an online review platform affects purchase intention and consumer engagement. Utilizing both controlled experiments and analysis of real-world reviews, I demonstrate that humorous appropriation attenuates the effect of review valence on purchase intentions and increases consumer engagement. In study three, I investigate how community ratings are related to patient treatment evaluations and compliance in an online health community. I find that community ratings are positively associated with treatment evaluations and compliance. Moreover, I find that community size and ratings variance moderate the effect of community ratings on treatment evaluations and compliance. Taken together, these essays contribute to the literature on Information Systems by augmenting the understanding of the effects of different types of user-generated content on social (status, engagement, and evaluations) and economic outcomes (purchase intentions and sales). The studies also offer insights for strategic decisions regarding user-generated content in online platforms.<br>Temple University--Theses
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Svensson, Kristoffer. "Sentiment Analysis With Convolutional Neural Networks : Classifying sentiment in Swedish reviews." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-64768.

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Today many companies exist and market their products and services on social medias, and therefore may receive reviews and thoughts from their end-users directly in these social medias. Reading every text by hand can be time-consuming, so by analysing the sentiment for all texts give the companies an overview how positive or negative the users are on a specific subject. Sentiment analysis is a feature that Beanloop AB is interested in implementing in their future projects and this thesis research problem was to investigate how deep learning could be used for this task. It was done by conducting an experiment with deep learning and neural networks. Several convolutional neural network models were implemented with different settings to find a combination of settings that gave the highest accuracy on the given test dataset. There were two different kind of models, one kind classifying positive and negative, and the second classified the previous two categories but also neutral. The training dataset and the test dataset contained data from two recommendation sites, www.reco.se and se.trustpilot.com. The final result shows that when classifying three categories (positive, negative and neutral) the models had problems to reach an accuracy at 85%, were only one model reached 80% accuracy as best on the test dataset. However, when only classifying two categories (positive and negative) the models showed very good results and reached almost 95% accuracy for every model.
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Kelley, Sharon M. "The Influence of Social Media| Effects of Online User-Generated Reviews on Customers' Perceptions and Business Profitability." Thesis, Capella University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13427927.

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<p> Online customer reviews (OCRs) have become an important part of the customer&rsquo;s shopping experience. The purpose of this qualitative exploratory single-case study was to investigate and better understand, how customers&rsquo; perceptions and experiences shared on OCRs or other social media outlets affected business revenue generation and profitability. The OCR phenomenon was explored from the consumer&rsquo;s perspective. The healthcare industry, private-practice sector was used as a case subject. However, business operations, not the field of medicine, was the focus of this study. Due to the general business focus of this study, the findings were applicable to other industries or sectors. The specific problem was that little research had been conducted to explore the influence of OCRs on customers&rsquo; perceptions and experiences, and the effects of those factors on the ability of small business owners to generate revenue and increase profitability. The theory of planned behavior (TPB) underpinned this study and provided the conceptual framework needed to understand better consumers&rsquo; attitudes, perceptions, behavior, and intentions. The research addressed a gap in knowledge and endeavored to provide small business owners with direct insight into the OCR phenomenon. Two research questions guided the study. First, how do OCRs influence consumer perceptions, experiences, and decision-making; and second, how do consumers&rsquo; perceptions and experiences influence how small business owners generate revenue and increase profitability. Data collection consisted of 11 in-depth interviews, 11 questionnaire respondents, 150 OCR ratings and comments, documentation, and archival records. An analysis of the data revealed 5 trends consumers considered: <i>experiences, perceptions, relationships, trust factors</i>, and <i>selection determinants </i>. The findings of this study confirmed that OCRs did have either a conscious or subliminal influence on consumers&rsquo; perceptions and experiences; thus, affecting how companies increase profitability.</p><p>
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Books on the topic "User reviews"

1

Lewis, Jane. EQFM excellence model: User handbook and guide for service reviews in local government. Woodward Lewis, 1999.

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Management, Office for Public. Joint reviews of social services departments: A framework for evaluating user and carer involvement. [The Office?], 1994.

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Foundation, British Quality, ed. The EFQM excellence model: User handbook and guide for service reviews in local government. Woodward Lewis, 1999.

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Jarvis, Robin. Users and uses of unlisted companies' financial statements: A literature review. Institute of Chartered Accountants in England and Wales, 1996.

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K, Luke Nancy, and Intelligent Buildings Institute Foundation, eds. Global protocol review & end-user needs analysis. The Foundation, 1993.

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Coley, John A. User authentication: A state-of-the-art review. Naval Postgraduate School, 1991.

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Stone, Sue. A review of user related research in humanities information. Consultancy and Research Unit, Department of Information Studies, University of Sheffield, 1985.

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Stone, Sue. A review of user related research in humanities information. University of Sheffield, Department of Information Studies,Consultancy and Research Unit, 1985.

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Stone, Sue. A review of user related research in humanities information. Consultancy and Research Unit, Dept. of Information Studies, University of Sheffield, 1985.

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Canada. Treasury Board. Evaluation, Audit and Review Group, ed. Review of the cost recovery and user fee approval process. Treasury Board of Canada, 1996.

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Book chapters on the topic "User reviews"

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Almishari, Mishari, and Gene Tsudik. "Exploring Linkability of User Reviews." In Computer Security – ESORICS 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33167-1_18.

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Muhammad, Azam Sheikh, Peter Damaschke, and Olof Mogren. "Summarizing Online User Reviews Using Bicliques." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49192-8_46.

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Terzi, Maria, Matthew Rowe, Maria-Angela Ferrario, and Jon Whittle. "Text-Based User-kNN: Measuring User Similarity Based on Text Reviews." In User Modeling, Adaptation, and Personalization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3_17.

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Lu, Yao, Xiangfei Kong, Xiaojun Quan, Wenyin Liu, and Yinlong Xu. "Exploring the Sentiment Strength of User Reviews." In Web-Age Information Management. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14246-8_46.

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Buitinck, Lars, Jesse van Amerongen, Ed Tan, and Maarten de Rijke. "Multi-emotion Detection in User-Generated Reviews." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16354-3_5.

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Ryang, Heungmo, and Unil Yun. "Ranking Book Reviews Based on User Discussion." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40675-1_2.

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Yun, Unil, and Heungmo Ryang. "Ranking Book Reviews Based on User Influence." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6738-6_17.

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Zhu, Miaoqi, and Xiaowen Fang. "Developing Playability Heuristics for Computer Games from Online Reviews." In Design, User Experience, and Usability. Theories, Methods, and Tools for Designing the User Experience. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07668-3_48.

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Pumpurs, Alberts. "User Journey Map as a Method to Extrapolate User Experience Knowledge from User Generated Reviews." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16947-2_14.

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Rossetti, Marco, Fabio Stella, Longbing Cao, and Markus Zanker. "Analysing User Reviews in Tourism with Topic Models." In Information and Communication Technologies in Tourism 2015. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14343-9_4.

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Conference papers on the topic "User reviews"

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Yeboah, Jones, and Saheed Popoola. "Analyzing User Sentiments Towards Static Analysis Tools: A Study Using User Reviews." In 2024 IEEE 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings). IEEE, 2024. https://doi.org/10.1109/aibthings63359.2024.10863289.

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Meng, Xinfan, and Houfeng Wang. "Mining user reviews." In the ACL-IJCNLP 2009 Conference Short Papers. Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1667583.1667637.

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Ciurumelea, Adelina, Sebastiano Panichella, and Harald C. Gall. "Automated user reviews analyser." In ICSE '18: 40th International Conference on Software Engineering. ACM, 2018. http://dx.doi.org/10.1145/3183440.3194988.

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Hale, Scott A. "User Reviews and Language." In CHI'16: CHI Conference on Human Factors in Computing Systems. ACM, 2016. http://dx.doi.org/10.1145/2851581.2892466.

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Habib, Anam, Furqan Khan Saddozai, Anum Sattar, Aurangzeb Khan, Ibrahim A. Hameed, and Fazal Masud Kundi. "User Intention Mining in Bussiness Reviews: A Review." In 2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC). IEEE, 2018. http://dx.doi.org/10.1109/besc.2018.8697303.

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Zhao, Feifei, Qizhi Qiu, and Wenyan Zhou. "A User Classification Solution Based on Users' Reviews." In 2014 13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES). IEEE, 2014. http://dx.doi.org/10.1109/dcabes.2014.53.

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Feng, Xingjie, Yunze Zeng, and Yixiong Xu. "Recommendation Algorithm for Federated User Reviews and Item Reviews." In the 2018 International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3293663.3293667.

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Merdenyan, Burak, and Helen Petrie. "User Reviews of Gamepad Controllers." In CHI PLAY '15: The annual symposium on Computer-Human Interaction in Play. ACM, 2015. http://dx.doi.org/10.1145/2793107.2810332.

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Tripathy, Amiya Kumar, Revathy Sundararajan, Chinmay Deshpande, Pankaj Mishra, and Neha Natarajan. "Opinion mining from user reviews." In 2015 International Conference on Technologies for Sustainable Development (ICTSD). IEEE, 2015. http://dx.doi.org/10.1109/ictsd.2015.7095904.

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Zeng, Fanxing. "Review rating model based on subjective vocabulary in user reviews." In International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), edited by Valentina E. Balas and Zeashan Hameed Khan. SPIE, 2023. http://dx.doi.org/10.1117/12.2682344.

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Reports on the topic "User reviews"

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Chevalier, Judith, Yaniv Dover, and Dina Mayzlin. Channels of Impact: User Reviews when Quality is Dynamic and Managers Respond. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23299.

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Rashid, Kazi Harunur, and Wahid bin Ahsan. User Trust in E-Commerce through Product List Pages, Detail Pages, Reviews, and Security Features. Userhub, 2024. http://dx.doi.org/10.58947/journal.dmvq89.

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This study investigates how design elements on Product List Pages (PLPs) and Product Detail Pages (PDPs) impact user trust in e-commerce. Using a mixed-methods approach with 65 survey respondents and 22 interview participants, the research reveals that consistency in UI design, high-quality product images, detailed descriptions, verified customer reviews, and visible security features are critical in fostering user trust. Well-structured navigation, accurate product information, and robust security measures significantly enhance trust and influence purchasing decisions, while misleading product descriptions and unreliable payment methods detract from user confidence. The study provides practical recommendations to optimize platform design, focusing on consistent interface elements, improved product imagery, and secure payment options.
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Agarwal, Smisha, Madhu Jalan, Holly C. Wilcox, et al. Evaluation of Mental Health Mobile Applications. Agency for Healthcare Research and Quality (AHRQ), 2022. http://dx.doi.org/10.23970/ahrqepctb41.

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Background. Mental health mobile applications (apps) have the potential to expand the provision of mental health and wellness services to traditionally underserved populations. There is a lack of guidance on how to choose wisely from the thousands of mental health apps without clear evidence of safety, efficacy, and consumer protections. Purpose. This Technical Brief proposes a framework to assess mental health mobile applications with the aim to facilitate selection of apps. The results of applying the framework will yield summary statements on the strengths and limitations of the apps and are intended for use by providers and patients/caregivers. Methods. We reviewed systematic reviews of mental health apps and reviewed published and gray literature on mental health app frameworks, and we conducted four Key Informant group discussions to identify gaps in existing mental health frameworks and key framework criteria. These reviews and discussions informed the development of a draft framework to assess mental health apps. Iterative testing and refinement of the framework was done in seven successive rounds through double application of the framework to a total of 45 apps. Items in the framework with an interrater reliability under 90 percent were discussed among the evaluation team for revisions of the framework or guidance. Findings. Our review of the existing frameworks identified gaps in the assessment of risks that users may face from apps, such as privacy and security disclosures and regulatory safeguards to protect the users. Key Informant discussions identified priority criteria to include in the framework, including safety and efficacy of mental health apps. We developed the Framework to Assist Stakeholders in Technology Evaluation for Recovery (FASTER) to Mental Health and Wellness and it comprises three sections: Section 1. Risks and Mitigation Strategies, assesses the integrity and risk profile of the app; Section 2. Function, focuses on descriptive aspects related to accessibility, costs, organizational credibility, evidence and clinical foundation, privacy/security, usability, functions for remote monitoring of the user, access to crisis services, and artificial intelligence (AI); and Section 3. Mental Health App Features, focuses on specific mental health app features, such as journaling and mood tracking. Conclusion. FASTER may be used to help appraise and select mental health mobile apps. Future application, testing, and refinements may be required to determine the framework’s suitability and reliability across multiple mental health conditions, as well as to account for the rapidly expanding applications of AI, gamification, and other new technology approaches.
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Sharova, Iryna. WAYS OF PROMOTING UKRANIAN PUBLISHING HOUSES ON FACEBOOK DURING QUARANTINE. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.49.11076.

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The article reviews and analyzes the promotion of Ukrainian publishing houses on Facebook during quarantine in 2020. The study’s main objective is content and its types, which were used for representing on Facebook. We found out that going live and posting a text with a picture was most popular. The phenomenon of live video is tightly connected to the quarantine phenomenon. Though, not every publishing house was able to go live permanently or at least regular. However, simple text with a picture is the most uncomplicated content to post and the most popular. Ukrainian publishers also use UGC (User Generated Content), situational content, and different contexts. The biggest problem for Ukrainian publishers is continual strategic work with social media for promotion. During quarantine, social media became the first channel for communication with customers and subscribers. Therefore promotion on the Internet and in social media indeed should become equivalent to offline promotion.
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Atabey, Ayça, Cory Robinson, Anna Lindroos Cermakova, Andra Siibak, and Natalia Ingebretsen Kucirkova. Ethics in EdTech: Consolidating Standards For Responsible Data Handling And Usercentric Design. University in Stavanger, 2024. http://dx.doi.org/10.31265/usps.283.

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This report proposes aspirational principles for EdTech providers, emphasizing ethical practices, robust data protection, ownership rights, transparent consent processes, and active user engagement, particularly with children. These measures aim to enhance transparency, accountability, and trust in EdTech platforms. Focusing on the K12 sector, the report systematically reviews and integrates key academic, legal, and technical frameworks to propose ethical benchmarks for the EdTech industry. The benchmarks go beyond quality assurance, highlighting good practices and ethical leadership for the field. The report addresses the need for a new culture in EdTech ethics, one that is collaborative and views EdTech providers as partners in dialogue with researchers and policy-makers to identify constructive solutions and uphold social trust. The outlined benchmarks are intended for national policymakers, international agencies, and certification bodies to consider when developing quality standards for EdTech used in schools. They include AI safeguards and stress the importance of meeting international data protection standards, establishing clear ownership rights, and implementing transparent consent processes to address data control issues, as well as active user engagement for improving data governance practices.
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Li, Lingxi, Yaobin Chen, Renren Tian, Feng Li, Howell Li, and James R. Sturdevant. An Integrated Critical Information Delivery Platform for Smart Segment Dissemination to Road Users. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317440.

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An integrated critical information delivery platform for smart segment dissemination to road users was developed. A statewide baseline milepost geodatabase was created at 0.1-mile resolution with tools, protocols, and interfaces that allow other data sources to be efficiently utilized. A variety of data sources (e.g., INRIX, CARS, Doppler, camera images, connected vehicle data, automated vehicle location) were integrated into existing and new dashboards for stakeholders to monitor roadway conditions and after-action reviews. Additionally, based on these data sources, algorithms were developed and an API was created to identify hazardous road conditions when the location of the end-user mobile device was given. Message delivery schemes were successfully implemented to issue alerts to drivers, which were integrated with two in-vehicle smartphone applications. The performance of the integrated platform was evaluated using both the driving simulator and a number of simulated and on-road tests. The results demonstrated the system was able to disseminate data in real-time using the developed platform.
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Scarpini, Celeste, Oyebola Okunogbe, and Fabrizio Santoro. The Promise and Limitations of Information Technology for Tax Mobilisation. Institute of Development Studies, 2023. http://dx.doi.org/10.19088/ictd.2023.005.

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As digital technologies continue gaining momentum in Africa and lower-income countries, more and more tax authorities are adopting them to improve their core functions and collect revenue more efficiently. This paper reviews recent literature on using technology for tax administration. Technology has the potential to improve tax collection in three areas: identifying the tax base, monitoring compliance, and facilitating compliance. But even the most user-friendly technology will hardly function without basic infrastructure and a stable internet connection. The potential benefits of new technology are further hampered by resistance from taxpayers and collectors, an unsupportive regulatory environment and lack of strategy for adoption by institutions. We close by proposing reforms to ensure investments in new technology improve efficiency and revenue collection.
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Murad, M. Hassan, Stephanie M. Chang, Celia Fiordalisi, et al. Improving the Utility of Evidence Synthesis for Decision Makers in the Face of Insufficient Evidence. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepcwhitepaperimproving.

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Background: Healthcare decision makers strive to operate on the best available evidence. The Agency for Healthcare Research and Quality Evidence-based Practice Center (EPC) Program aims to support healthcare decision makers by producing evidence reviews that rate the strength of evidence. However, the evidence base is often sparse or heterogeneous, or otherwise results in a high degree of uncertainty and insufficient evidence ratings. Objective: To identify and suggest strategies to make insufficient ratings in systematic reviews more actionable. Methods: A workgroup comprising EPC Program members convened throughout 2020. We conducted interative discussions considering information from three data sources: a literature review for relevant publications and frameworks, a review of a convenience sample of past systematic reviews conducted by the EPCs, and an audit of methods used in past EPC technical briefs. Results: Several themes emerged across the literature review, review of systematic reviews, and review of technical brief methods. In the purposive sample of 43 systematic reviews, the use of the term “insufficient” covered both instances of no evidence and instances of evidence being present but insufficient to estimate an effect. The results of the literature review and review of the EPC Program systematic reviews illustrated the importance of clearly stating the reasons for insufficient evidence. Results of both the literature review and review of systematic reviews highlighted the factors decision makers consider when making decisions when evidence of benefits or harms is insufficient, such as costs, values, preferences, and equity. We identified five strategies for supplementing systematic review findings when evidence on benefit or harms is expected to be or found to be insufficient, including: reconsidering eligible study designs, summarizing indirect evidence, summarizing contextual and implementation evidence, modelling, and incorporating unpublished health system data. Conclusion: Throughout early scoping, protocol development, review conduct, and review presentation, authors should consider five possible strategies to supplement potential insufficient findings of benefit or harms. When there is no evidence available for a specific outcome, reviewers should use a statement such as “no studies” instead of “insufficient.” The main reasons for insufficient evidence rating should be explicitly described.
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Bozek, Michael, and Tani Hubbard. Greater Yellowstone Network amphibian monitoring protocol science review: A summary of reviewers’ responses. National Park Service, 2022. http://dx.doi.org/10.36967/nrr-2293614.

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Science reviews are an essential cornerstone of all excellent science programs and are a requirement of monitoring programs within the Inventory and Monitoring Division of the National Park Service (NPS). Science reviews provide necessary professional critique of objectives, study design, data collection, analysis, scientific interpretation, and how effectively information is transferred to target audiences. Additionally, reviews can help identify opportunities to cooperate more effectively with interested and vested partners to expand the impacts of collective findings across larger landscapes. In December 2020, seven biologists from USGS, USFWS, and NPS provided a critical review of the Greater Yellowstone Network Amphibian Monitoring Protocol for monitoring Columbia spotted frogs (Rana luteiventris), boreal chorus frogs (Pseudacris maculata), western toads (Anaxyrus boreas), western tiger salamanders (Ambystoma mavortium), and environmental conditions at wetland sites clustered within watershed units in Yellowstone and Grand Teton national parks. This review followed sixteen years of GRYN amphibian and wetland monitoring, allowing us to evaluate the impact of the work thus far and to discuss potential improvements to the protocol. Reviewers were asked to assess the following amphibian monitoring objectives per Bennetts et al. (2013, Cooperative amphibian monitoring protocol for the Greater Yellowstone Network: Narrative, version 1.0, https://irma.nps.gov/DataStore/Reference/Profile/2194571) and to assess the degree to which GRYN is meeting the objectives based on the current sampling, analyses, and reporting: Objective 1: Estimate the proportion of catchments and wetland sites used for breeding by each of the four common, native amphibian species annually, and estimate the rate at which their use is changing over time. Objective 2: Determine the total number of wetlands within sampled catchments that are suitable for amphibian breeding (i.e., have standing water during the breeding season) annually. Objective 3: For western toads, estimate the proportion of previously identified breeding areas that are used annually, and estimate the rate at which their use may be changing over time. Generally, reviewers commended the GRYN Amphibian Monitoring Program, including the design, the statistical rigor of current analytical approaches, the large number of monitoring reports and publications, and the audiences reached. Reviewers unanimously felt that the first two objectives of this protocol are being met for two species (Columbia spotted frogs and boreal chorus frogs) in medium- and high-quality catchments, and all but one reviewer also felt these objectives are being met for western tiger salamanders. It was universally recognized that objective 3 for western toads is not being met but reviewers attributed this to issues related to funding and capacity rather than design flaws. Reviewers felt the current design provides an adequate base for parlaying additional work and offered suggestions focused on increasing efficiencies, maximizing information that can be collected in the field, strengthening analyses, and improving scientific outreach. In this document, we summarize reviewers' comments and include their full written reviews in Appendix B.
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Morville, Anne-Le, Janice Jones, Michal Avrech-Bar, et al. A scoping review protocol on Occupational Science Research in European Contexts. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.7.0056.

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Review question / Objective: Researchers may conduct scoping reviews instead of systematic reviews where the purpose of thereview is to identify knowledge gaps, scope a body of literature, clarify concepts or to investigate research conduct. While useful in their own right, scoping reviews may also be helpful precursors to systematic reviews and can be used to confirm the relevance of inclusion criteria and potential questions. (Munn et al. BMC Medical Research Methodology (2018) 18:143) The aim of this review is to scope the empirical-based and peer-reviewed European OS research literature and map identified research methods, theories or theoretical concepts, and target groups to obtain a status quo overview of OS research undertaken in Europe between 2015 and 2020. Research questions: • What recent development is seen when mapping the empirical-based and peer-reviewed European OS research literature in accordance with publication volume, publication date and geographical context? • What characterizes the identified research methods, theories or theoretical concepts, and target groups applied in the peer-reviewed OS research literature?
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