Academic literature on the topic 'Crowd-sourced opinions'
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Journal articles on the topic "Crowd-sourced opinions"
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Lee, Michael D., and Michelle Y. Ke. "Framing effects and preference reversals in crowd-sourced ranked opinions." Decision 9, no. 2 (April 2022): 153–71. http://dx.doi.org/10.1037/dec0000166.
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Chen, Min, and Anusha Prabakaran. "Credibility Analysis for Online Product Reviews." International Journal of Multimedia Data Engineering and Management 9, no. 3 (July 2018): 37–54. http://dx.doi.org/10.4018/ijmdem.2018070103.
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With the prevalence of e-commerce, online product reviews are increasingly considered crowd-sourced consumer opinions that significantly influence customer purchasing decisions and product rankings. It is therefore important to ensure the truthfulness of reviews by detecting and filtering out fake/spam reviews. This article presents an effective framework to analyze review credibility for spam detection and opinion mining. It incorporates three methods: duplicated review detection, anomaly detection, and incentivized review detection, that complement each other to produce statistical credibility scores indicating review credibility. A practical end-to-end system is designed and developed accordingly, and is equipped with high-level data visualization for easy interpretation and summarization of the analysis results. Experiments on an Amazon review dataset demonstrate its efficiency, scalability and accuracy. This system could help e-commerce and consumers identify fake reviews, refine product rankings, and constrain vendors and spammers from engaging in dishonest practices.
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Sherbino, Jonathan, Nikita Joshi, and Michelle Lin. "JGME-ALiEM Hot Topics in Medical Education Online Journal Club: An Analysis of a Virtual Discussion About Resident Teachers." Journal of Graduate Medical Education 7, no. 3 (September 1, 2015): 437–44. http://dx.doi.org/10.4300/jgme-d-15-00071.1.
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ABSTRACT Background In health professionals' education, senior learners play a key role in the teaching of junior colleagues. Objective We describe an online discussion about residents as teachers to highlight the topic and the online journal club medium. Methods In January 2015, the Journal of Graduate Medical Education (JGME) and the Academic Life in Emergency Medicine blog facilitated an open-access, online, weeklong journal club on the JGME article “What Makes a Great Resident Teacher? A Multicenter Survey of Medical Students Attending an Internal Medicine Conference.” Social media platforms used to promote asynchronous discussions included a blog, a video discussion via Google Hangouts on Air, and Twitter. We performed a thematic analysis of the discussion. Web analytics were captured as a measure of impact. Results The blog post garnered 1324 page views from 372 cities in 42 countries. Twitter was used to endorse discussion points, while blog comments provided opinions or responded to an issue. The discussion focused on why resident feedback was devalued by medical students. Proposed explanations included feedback not being labeled as such, the process of giving delivery, the source of feedback, discrepancies with self-assessment, and threats to medical student self-image. The blog post resulted in a crowd-sourced repository of resident teacher resources. Conclusions An online journal club provides a novel discussion forum across multiple social media platforms to engage authors, content experts, and the education community. Crowd-sourced analysis of the resident teacher role suggests that resident feedback to medical students is important, and barriers to student acceptance of feedback can be overcome.
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Sahbuddin, Muktar, and Surya Agustian. "Support Vector Machine Method with Word2vec for Covid-19 Vaccine Sentiment Classification on Twitter." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, no. 1 (July 23, 2022): 288–97. http://dx.doi.org/10.31289/jite.v6i1.7534.
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Covid-19 has been a dangerous outbreak for the world that has lasted more than 2 years. Covid-19 has evolved or developed into several new variations, such as delta which is more dangerous than its initial variant. Vaccines became the world's solution to defend against Covid-19. In Indonesia, at the early stages of implementing mass vaccination programs, people had been involved in many pros and cons, to support or against the program. On social media such as Twitter, public opinions about vaccines are very diverse. This study investigates public sentiment towards the early stage of vaccination program conducted by the government. The classification method used in the sentiment analysis is the Support Vector Machine (SVM), among the positive, negative and neutral classes, with word embeddings extraction features. Data was collected and labeled by 12 crowd sourced annotators. The training and parameter tuning process was carried out to find the model that produced the best accuracy of validation data. From 400 testing data, the application of this optimal model resulted in an F1-score of 65% and an accuracy of 69%, higher than several machine learning methods in the same study.
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Padhye, Nikhil. "Bayesian Measurement of Diagnostic Accuracy of the RT-PCR Test for COVID-19." Metrology 2, no. 4 (September 29, 2022): 414–26. http://dx.doi.org/10.3390/metrology2040025.
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Reverse transcription polymerase chain reaction (RT-PCR) targeting select genes of the SARS-CoV-2 RNA has been the main diagnostic tool in the global response to the COVID-19 pandemic. It took several months after the development of these molecular tests to assess their diagnostic performance in the population. The objective of this study is to demonstrate that it was possible to measure the diagnostic accuracy of the RT-PCR test at an early stage of the pandemic despite the absence of a gold standard. The study design is a secondary analysis of published data on 1014 patients in Wuhan, China, of whom 59.3% tested positive for COVID-19 in RT-PCR tests and 87.6% tested positive in chest computerized tomography (CT) exams. Previously ignored expert opinions in the form of verbal probability classifications of patients with conflicting test results have been utilized here to derive the informative prior distribution of the infected proportion. A Bayesian implementation of the Dawid-Skene model, typically used in the context of crowd-sourced data, was used to reconstruct the sensitivity and specificity of the diagnostic tests without the need for specifying a gold standard. The sensitivity of the RT-PCR diagnostic test developed by China CDC was estimated to be 0.707 (95% Cr I: 0.664, 0.753), while the specificity was 0.861 (95% Cr I: 0.781, 0.956). In contrast, chest CT was found to have high sensitivity (95% Cr I: 0.969, 1.000) but low specificity (95% Cr I: 0.477, 0.742). This estimate is similar to estimates that were found later in studies designed specifically for measuring the diagnostic performance of the RT-PCR test. The developed methods could be applied to assess diagnostic accuracy of new variants of SARS-CoV-2 in the future.
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Xun, Helen, Waverley He, Jonlin Chen, Scott Sylvester, Sheera F. Lerman, and Julie Caffrey. "Characterization and Comparison of the Utilization of Facebook Groups Between Public Medical Professionals and Technical Communities to Facilitate Idea Sharing and Crowdsourcing During the COVID-19 Pandemic: Cross-sectional Observational Study." JMIR Formative Research 5, no. 4 (April 30, 2021): e22983. http://dx.doi.org/10.2196/22983.
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Background Strict social distancing measures owing to the COVID-19 pandemic have led people to rely more heavily on social media, such as Facebook groups, as a means of communication and information sharing. Multiple Facebook groups have been formed by medical professionals, laypeople, and engineering or technical groups to discuss current issues and possible solutions to the current medical crisis. Objective This study aimed to characterize Facebook groups formed by laypersons, medical professionals, and technical professionals, with specific focus on information dissemination and requests for crowdsourcing. Methods Facebook was queried for user-created groups with the keywords “COVID,” “Coronavirus,” and “SARS-CoV-2” at a single time point on March 31, 2020. The characteristics of each group were recorded, including language, privacy settings, security requirements to attain membership, and membership type. For each membership type, the group with the greatest number of members was selected, and in each of these groups, the top 100 posts were identified using Facebook’s algorithm. Each post was categorized and characterized (evidence-based, crowd-sourced, and whether the poster self-identified). STATA (version 13 SE, Stata Corp) was used for statistical analysis. Results Our search yielded 257 COVID-19–related Facebook groups. Majority of the groups (n=229, 89%) were for laypersons, 26 (10%) were for medical professionals, and only 2 (1%) were for technical professionals. The number of members was significantly greater in medical groups (21,215, SD 35,040) than in layperson groups (7623, SD 19,480) (P<.01). Medical groups were significantly more likely to require security checks to attain membership (81% vs 43%; P<.001) and less likely to be public (3 vs 123; P<.001) than layperson groups. Medical groups had the highest user engagement, averaging 502 (SD 633) reactions (P<.01) and 224 (SD 311) comments (P<.01) per post. Medical professionals were more likely to use the Facebook groups for education and information sharing, including academic posts (P<.001), idea sharing (P=.003), resource sharing (P=.02) and professional opinions (P<.001), and requesting for crowdsourcing (P=.003). Layperson groups were more likely to share news (P<.001), humor and motivation (P<.001), and layperson opinions (P<.001). There was no significant difference in the number of evidence-based posts among the groups (P=.10). Conclusions Medical professionals utilize Facebook groups as a forum to facilitate collective intelligence (CI) and are more likely to use Facebook groups for education and information sharing, including academic posts, idea sharing, resource sharing, and professional opinions, which highlights the power of social media to facilitate CI across geographic distances. Layperson groups were more likely to share news, humor, and motivation, which suggests the utilization of Facebook groups to provide comedic relief as a coping mechanism. Further investigations are necessary to study Facebook groups’ roles in facilitating CI, crowdsourcing, education, and community-building.
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Nguyen, Dong, Barbara McGillivray, and Taha Yasseri. "Emo, love and god: making sense of Urban Dictionary, a crowd-sourced online dictionary." Royal Society Open Science 5, no. 5 (May 2018): 172320. http://dx.doi.org/10.1098/rsos.172320.
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The Internet facilitates large-scale collaborative projects and the emergence of Web 2.0 platforms, where producers and consumers of content unify, has drastically changed the information market. On the one hand, the promise of the ‘wisdom of the crowd’ has inspired successful projects such as Wikipedia, which has become the primary source of crowd-based information in many languages. On the other hand, the decentralized and often unmonitored environment of such projects may make them susceptible to low-quality content. In this work, we focus on Urban Dictionary, a crowd-sourced online dictionary. We combine computational methods with qualitative annotation and shed light on the overall features of Urban Dictionary in terms of growth, coverage and types of content. We measure a high presence of opinion-focused entries, as opposed to the meaning-focused entries that we expect from traditional dictionaries. Furthermore, Urban Dictionary covers many informal, unfamiliar words as well as proper nouns. Urban Dictionary also contains offensive content, but highly offensive content tends to receive lower scores through the dictionary’s voting system. The low threshold to include new material in Urban Dictionary enables quick recording of new words and new meanings, but the resulting heterogeneous content can pose challenges in using Urban Dictionary as a source to study language innovation.
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Mohd, Mudasir, Rafiya Jan, and Nida Hakak. "Enhanced Bootstrapping Algorithm for Automatic Annotation of Tweets." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 35–60. http://dx.doi.org/10.4018/ijcini.2020040103.
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Annotations are critical in various text mining tasks such as opinion mining, sentiment analysis, word sense disambiguation. Supervised learning algorithms start with the training of the classifier and require manually annotated datasets. However, manual annotations are often subjective, biased, onerous, and burdensome to develop; therefore, there is a need for automatic annotation. Automatic annotators automatically annotate the data for creating the training set for the supervised classifier, but lack subjectivity and ignore semantics of underlying textual structures. The objective of this research is to develop scalable and semantically rich automatic annotation system while incorporating domain dependent characteristics of the annotation process. The authors devised an enhanced bootstrapping algorithm for the automatic annotation of Tweets and employed distributional semantic models (LSA and Word2Vec) to augment the novel Bootstrapping algorithm and tested the proposed algorithm on the 12,000 crowd-sourced annotated Tweets and achieved a 68.56% accuracy which is higher than the baseline accuracy.
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Kloker, Simon. "Crowd-sourced Manipulation and Fraud Detection." Journal of Prediction Markets 15, no. 1 (July 13, 2021). http://dx.doi.org/10.5750/jpm.v15i1.1846.
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Prediction markets are a common tool of companies for idea management and evaluation during the innovation process, which enables them to include expectations and opinions of stakeholders across organizational boundaries. However, prediction markets are also known for their susceptibility to manipulation in theory and practice. The irregular and multifaceted occurrence of these phenomena, with sometimes very creative strategies, makes it difficult to detect manipulation and fraud based on algorithms. To ensure robust and reliable forecasts, which are of utmost importance for a focused and successful digital innovation process, there is a need for a monitoring approach capable of dealing with these specific problems. In an Action Design Research project, we address this problem by developing a crowd-sourced manipulation and fraud detection tool. The artifact enables the crowd to successfully decompose the large set of trading data and successfully find even creative strategies without guidance. The artifact is implemented and evaluated in the field in the prediction market [blinded for review]. We conclude, that a crowd-sourced approach can be suggested to monitor ambiguous and rare events with a varying character in our context and presumably other contexts as well.
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Yang, Zhiyong, Qianqian Xu, Xiaochun Cao, and Qingming Huang. "From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions." Proceedings of the AAAI Conference on Artificial Intelligence 32, no. 1 (April 25, 2018). http://dx.doi.org/10.1609/aaai.v32i1.11258.
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Visual attributes, which refer to human-labeled semantic annotations, have gained increasing popularity in a wide range of real world applications. Generally, the existing attribute learning methods fall into two categories: one focuses on learning user-specific labels separately for different attributes, while the other one focuses on learning crowd-sourced global labels jointly for multiple attributes. However, both categories ignore the joint effect of the two mentioned factors: the personal diversity with respect to the global consensus; and the intrinsic correlation among multiple attributes. To overcome this challenge, we propose a novel model to learn user-specific predictors across multiple attributes. In our proposed model, the diversity of personalized opinions and the intrinsic relationship among multiple attributes are unified in a common-to-special manner. To this end, we adopt a three-component decomposition. Specifically, our model integrates a common cognition factor, an attribute-specific bias factor and a user-specific bias factor. Meanwhile Lasso and group Lasso penalties are adopted to leverage efficient feature selection. Furthermore, theoretical analysis is conducted to show that our proposed method could reach reasonable performance. Eventually, the empirical study carried out in this paper demonstrates the effectiveness of our proposed method.
Dissertations / Theses on the topic "Crowd-sourced opinions"
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Du, Jiahua. "Advanced Review Helpfulness Modeling." Thesis, 2020. https://vuir.vu.edu.au/41279/.
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In recent years, online shopping has gained immense popularity due to its feedback mechanism. By composing online comments, previous buyers share opinions and expe-riences regarding the items that they have purchased. These user-generated reviews, in turn, provide valuable information to potential customers in regards to deciding which products to purchase. The reviews also help vendors understand customer needs and improve product quality. Yet despite these benefits, the unprecedentedly rapid growth of user-generated content has overwhelmed human ability in online review scrutiny. On-line reviews that possess varying content further impedes useful knowledge distillation. The large volume of online reviews that are uneven in quality puts growing pressure on automatic approaches for effective review utilization and informative content prioritiza-tion.
Review helpfulness prediction leverages machine learning methods to identify and recommend helpful reviews to customers. In particular, review characteristics form the backbone of helpfulness information acquisition. Prior literature has observed and as-sociated a large body of determinants with review helpfulness. However, these deter-minants heavily rely on the domain knowledge of experts. The selection of and the interaction between the determinants also remain understudied, leaving ample room for exploration. The general lack of systematic experiment protocols among the existing methods further harms the task’s reproducibility, comparability, and generalizability.
This thesis aims to automatically model helpfulness information from online user- generated reviews. The thesis proposes effective modeling techniques and novel so-lutions to tackle the aforementioned challenges, with more emphasis on sophisticated feature learning and interaction. The thesis has made the following contributions to standardize the research field and advance the accuracy in helpfulness prediction.
1. A comprehensive survey is conducted to identify frequently used content-based determinants for automatic helpfulness prediction. A computational framework is developed to empirically evaluate the identified features across domains. Three selection scenarios are considered for feature behavior analysis. The domain-specific and domain-independent feature selection guidelines are summarized to facilitate future research prototyping. The implementation details of the study are discussed to standardize the task of automatic helpfulness prediction.
2. A deep neural framework is designed to enrich the interaction between review texts and star ratings during automatic helpfulness prediction. A gated convolu-tional component is introduced to learns content representations. A gated em- bedding method is proposed for encoding sophisticated yet adaptive rating infor- mation. An element alignment mechanism is proposed to explicitly capture the text-rating interaction. Ablation studies and qualitative analysis are conducted to discover insights into the interactive behavior of star ratings.
3. An end-to-end neural architecture is proposed to contextualize automatic helpful- ness prediction using review neighbors. Four weighting schemes are designed to encode a review’s surrounding neighbors as its context information into content representation learning. Three types of reviews neighbors of varied length are considered during context construction. Finally, discussions on the experimental results and the trade-o between model complexity and performance are given, along with case studies, to understand the proposed architecture.
Book chapters on the topic "Crowd-sourced opinions"
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Abenga, Elizabeth Sarange Bosire, Elijah Owuor Okono, Mzee Awuor, and Sarah Otanga. "Framework for Technology-Enriched Active Class Learning of Physics in Secondary Schools in Kenya." In Digital Solutions and the Case for Africa’s Sustainable Development, 131–51. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2967-6.ch009.
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Active learning transforms the learning process and activities from tutor focused to learner-cantered and is driven by the learner's learning ability. In other words, active learning provides an opportunity for self-directed learning that enables the learners to engage with the learning materials at personal level and pace. Thus, this chapter argues that active learning can provide equal learning opportunity for every single learner irrespective of the differences in their personality traits that would otherwise affect how they learn. Hence, this chapter proposes a framework for technology-enriched active learning for young learners that provides a personalized learning that deviates from the traditional “fit-for-all” classroom setups that tends to favour only the extrovert students. The proposed framework leverages advancement in technology such as personal learning network, virtual physics labs, massive open online courses, and crowd-sourced expert opinions to provide the learners with just-in-time active learning opportunity.
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Hai-Jew, Shalin. "Multidimensional Mappings of Political Accounts for Malicious Political Socialbot Identification." In Global Cyber Security Labor Shortage and International Business Risk, 263–348. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5927-6.ch012.
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Malicious political socialbots used to sway public opinion regarding the U.S. government and its functions have been identified as part of a larger information warfare effort by the Russian government. This work asks what is knowable from a web-based sleuthing approach regarding the following four factors: 1) the ability to identify malicious political socialbot accounts based on their ego neighborhoods at 1, 1.5, and 2 degrees; 2) the ability to identify malicious political socialbot accounts based on the claimed and linked geographical locations of their accounts, their ego neighborhoods, and their #hashtag networks; 3) the ability to identify malicious political socialbot accounts based on their strategic messaging (content, sentiment, and language structures) on respective social media platforms; and 4) the ability to identify and describe “maliciousness” in malicious political socialbot accounts based on observable behaviors on that account on three social media platform types: (a) microblogging, (b) social networking, and (c) crowd-sourced encyclopedia content sharing.
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Hai-Jew, Shalin. "Multidimensional Mappings of Political Accounts for Malicious Political Socialbot Identification." In Research Anthology on Combating Cyber-Aggression and Online Negativity, 911–94. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5594-4.ch050.
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Malicious political socialbots used to sway public opinion regarding the U.S. government and its functions have been identified as part of a larger information warfare effort by the Russian government. This work asks what is knowable from a web-based sleuthing approach regarding the following four factors: 1) the ability to identify malicious political socialbot accounts based on their ego neighborhoods at 1, 1.5, and 2 degrees; 2) the ability to identify malicious political socialbot accounts based on the claimed and linked geographical locations of their accounts, their ego neighborhoods, and their #hashtag networks; 3) the ability to identify malicious political socialbot accounts based on their strategic messaging (content, sentiment, and language structures) on respective social media platforms; and 4) the ability to identify and describe “maliciousness” in malicious political socialbot accounts based on observable behaviors on that account on three social media platform types: (a) microblogging, (b) social networking, and (c) crowd-sourced encyclopedia content sharing.
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Hai-Jew, Shalin. "Multidimensional Mappings of Political Accounts for Malicious Political Socialbot Identification." In Research Anthology on Social Media's Influence on Government, Politics, and Social Movements, 244–327. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7472-3.ch013.
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Malicious political socialbots used to sway public opinion regarding the U.S. government and its functions have been identified as part of a larger information warfare effort by the Russian government. This work asks what is knowable from a web-based sleuthing approach regarding the following four factors: 1) the ability to identify malicious political socialbot accounts based on their ego neighborhoods at 1, 1.5, and 2 degrees; 2) the ability to identify malicious political socialbot accounts based on the claimed and linked geographical locations of their accounts, their ego neighborhoods, and their #hashtag networks; 3) the ability to identify malicious political socialbot accounts based on their strategic messaging (content, sentiment, and language structures) on respective social media platforms; and 4) the ability to identify and describe “maliciousness” in malicious political socialbot accounts based on observable behaviors on that account on three social media platform types: (a) microblogging, (b) social networking, and (c) crowd-sourced encyclopedia content sharing.