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Fosch-Villaronga, Eduard, Adam Poulsen, Roger A. Søraa, and Bart Custers. "Gendering algorithms in social media." ACM SIGKDD Explorations Newsletter 23, no. 1 (May 26, 2021): 24–31. http://dx.doi.org/10.1145/3468507.3468512.
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Social media platforms employ inferential analytics methods to guess user preferences and may include sensitive attributes such as race, gender, sexual orientation, and political opinions. These methods are often opaque, but they can have significant effects such as predicting behaviors for marketing purposes, influencing behavior for profit, serving attention economics, and reinforcing existing biases such as gender stereotyping. Although two international human rights treaties include express obligations relating to harmful and wrongful stereotyping, these stereotypes persist both online and offline, and platforms often appear to fail to understand that gender is not merely a binary of being a 'man' or a 'woman,' but is socially constructed. Our study investigates the impact of algorithmic bias on inadvertent privacy violations and the reinforcement of social prejudices of gender and sexuality through a multidisciplinary perspective including legal, computer science, and queer media viewpoints. We conducted an online survey to understand whether and how Twitter inferred the gender of users. Beyond Twitter's binary understanding of gender and the inevitability of the gender inference as part of Twitter's personalization trade-off, the results show that Twitter misgendered users in nearly 20% of the cases (N=109). Although not apparently correlated, only 8% of the straight male respondents were misgendered, compared to 25% of gay men and 16% of straight women. Our contribution shows how the lack of attention to gender in gender classifiers exacerbates existing biases and affects marginalized communities. With our paper, we hope to promote the online account for privacy, diversity, and inclusion and advocate for the freedom of identity that everyone should have online and offline.
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Iliadis, Andrew. "Algorithms, ontology, and social progress." Global Media and Communication 14, no. 2 (May 22, 2018): 219–30. http://dx.doi.org/10.1177/1742766518776688.
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Recently, media and communication researchers have shown an increasing interest in critical data studies and ways to utilize data for social progress. In this commentary, I highlight several useful contributions in the International Panel on Social Progress (IPSP) report toward identifying key data justice issues, before suggesting extra focus on algorithmic discrimination and implicit bias. Following my assessment of the IPSP’s report, I emphasize the importance of two emerging media and communication areas – applied ontology and semantic technology – that impact internet users daily, yet receive limited attention from critical data researchers. I illustrate two examples to show how applied ontologies and semantic technologies impact social processes by engaging in the hierarchization of social relations and entities, a practice that will become more common as the Internet changes states towards a ‘smarter’ version of itself.
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A., Dr Pasumponpandian. "A Hybrid-Algorithm for E-project Selection on Social Media." June 2020 2, no. 2 (May 27, 2020): 116–22. http://dx.doi.org/10.36548/jitdw.2020.2.005.
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The integration of two of the biggest giants in the computing world has resulted in the development and advancement of new methodologies in data processing. Cognitive computing and big data analytics are integrated to give rise to advanced technologically sound algorithms like MOIWO and NSGA. There is an important role played by the E-projects portfolio selection (EPPS) issue in the web development environment that is handled with the help of a decision making algorithm based on big data. The EPPS problem tackles choosing the right projects for investment on the social media in order to achieve maximum return at minimal risk conditions. In order to address this issue and further optimize EPPS probe on social media, the proposed work focuses on building a hybrid algorithm known as NSGA-II-MOIWO. This algorithms makes use of the positive aspects of MOIWO algorithm and NSGA-II algorithm in order to develop an efficient one. The experimental results are recorded and analyzed in order to determine the most optimal algorithm based on the return and risk of investment. Based on the results, it is found that NSGA-II-MOIWO outperforms both MOIWO and NSGA, proving to be a better hybrid alternative.
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Nazarov, M. M. "Media Platforms and Algorithms: content and social implications." Communicology 8, no. 2 (June 30, 2020): 108–24. http://dx.doi.org/10.21453/2311-3065-2020-8-2-108-124.
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The paper considers the new features of the digital media environment associated with the widespread introduction of platforms and algorithms in media practices and reveals the technological, business and social background of these innovations. The application of platforms and algorithms is a powerful tool for implementing the commercial imperative in the media. In general, this is a characteristic feature of the development of modern society – a trend towards comprehensive metrization. Along with the advantages, the use of predictive algorithms, personalization of content based on tracking of past communicative behavior has a number of negative social consequences. E.g., ‘filter bubbles’ contribute to the formation of closed information segments. The model of social behaviorism underlying the recommendation services contributes to the modification of people’s informational behavior. Algorithmization of media landscape strengthens the trends of content delivery to individual consumers, and not to citizens inclined to make joint decisions regarding the common interests of social life.
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Petrescu, Maria, and Anjala S. Krishen. "The dilemma of social media algorithms and analytics." Journal of Marketing Analytics 8, no. 4 (November 5, 2020): 187–88. http://dx.doi.org/10.1057/s41270-020-00094-4.
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Karppi, Tero, and Kate Crawford. "Social Media, Financial Algorithms and the Hack Crash." Theory, Culture & Society 33, no. 1 (May 4, 2015): 73–92. http://dx.doi.org/10.1177/0263276415583139.
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Ajji, Kamel. "Cyborg finance mirrors cyborg social media." Big Data & Society 7, no. 1 (January 2020): 205395172093513. http://dx.doi.org/10.1177/2053951720935139.
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This article aims at showing the similarities between the financial and the tech sectors in their use and reliance on information and algorithms and how such dependency affects their attitude towards regulation. Drawing on Pasquale’s recommendations for reform, it sets out a proposal for a constant and independent scrutiny of internet service providers.
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Chaniotakis, Emmanouil, Constantinos Antoniou, Georgia Aifadopoulou, and Loukas Dimitriou. "Inferring Activities from Social Media Data." Transportation Research Record: Journal of the Transportation Research Board 2666, no. 1 (January 2017): 29–37. http://dx.doi.org/10.3141/2666-04.
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Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals.
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T.K., Balaji, Chandra Sekhara Rao Annavarapu, and Annushree Bablani. "Machine learning algorithms for social media analysis: A survey." Computer Science Review 40 (May 2021): 100395. http://dx.doi.org/10.1016/j.cosrev.2021.100395.
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Udanor, Collins, Stephen Aneke, and Blessing Ogechi Ogbuokiri. "Determining social media impact on the politics of developing countries using social network analytics." Program 50, no. 4 (September 6, 2016): 481–507. http://dx.doi.org/10.1108/prog-02-2016-0011.
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Purpose The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries. Design/methodology/approach Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms. Findings Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies. Practical implications With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics. Social implications Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB. Originality/value The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.
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Altunbey Ozbay, Feyza, and Bilal Alatas. "A Novel Approach for Detection of Fake News on Social Media Using Metaheuristic Optimization Algorithms." Elektronika ir Elektrotechnika 25, no. 4 (August 7, 2019): 62–67. http://dx.doi.org/10.5755/j01.eie.25.4.23972.
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Deceptive content is becoming increasingly dangerous, such as fake news created by social media users. Individuals and society have been affected negatively by the spread of low-quality news on social media. The fake and real news needs to be detected to eliminate the disadvantages of social media. This paper proposes a novel approach for fake news detection (FND) problem on social media. Applying this approach, FND problem has been considered as an optimization problem for the first time and two metaheuristic algorithms, the Grey Wolf Optimization (GWO) and Salp Swarm Optimization (SSO) have been adapted to the FND problem for the first time as well. The proposed FND approach consists of three stages. The first stage is data preprocessing. The second stage is adapting GWO and SSO for construction of a novel FND model. The last stage consists of using proposed FND model for testing. The proposed approach has been evaluated using three different real-world datasets. The results have been compared with seven supervised artificial intelligence algorithms. The results show GWO algorithm has the best performance in comparison with SSO algorithm and the other artificial intelligence algorithms. GWO seems to be efficiently used for solving different types of social media problems.
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Dokuz, Ahmet Sakir, and Mete Celik. "Cloud Computing-Based Socially Important Locations Discovery on Social Media Big Datasets." International Journal of Information Technology & Decision Making 19, no. 02 (March 2020): 469–97. http://dx.doi.org/10.1142/s0219622020500091.
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Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provides valuable information, such as which locations are frequently visited by a social media user, which locations are common for a social media user group, and which locations are socially important for a group of urban area residents. However, discovering socially important locations is challenging due to huge volume, velocity, and variety of social media datasets, inefficiency of current interest measures and algorithms on social media big datasets, and the need of massive spatial and temporal calculations for spatial social media analyses. In contrast, cloud computing provides infrastructure and platforms to scale compute-intensive jobs. In the literature, limited number of studies related to socially important locations discovery takes into account cloud computing systems to scale increasing dataset size and to handle massive calculations. This study proposes a cloud-based socially important locations discovery algorithm of Cloud SS-ILM to handle volume and variety of social media big datasets. In particular, in this study, we used Apache Hadoop framework and Hadoop MapReduce programming model to scale dataset size and handle massive spatial and temporal calculations. The performance evaluation of the proposed algorithm is conducted on a cloud computing environment using Turkey Twitter social media big dataset. The experimental results show that using cloud computing systems for socially important locations discovery provide much faster discovery of results than classical algorithms. Moreover, the results show that it is necessary to use cloud computing systems for analyzing social media big datasets that could not be handled with traditional stand-alone computer systems. The proposed Cloud SS-ILM algorithm could be applied on many application areas, such as targeted advertisement of businesses, social media utilization of cities for city planners and local governments, and handling emergency situations.
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Sarwani, Mohammad Zoqi, Dian Ahkam Sani, and Fitria Chabsah Fakhrini. "Personality Classification through Social Media Using Probabilistic Neural Network Algorithms." International Journal of Artificial Intelligence & Robotics (IJAIR) 1, no. 1 (October 31, 2019): 9. http://dx.doi.org/10.25139/ijair.v1i1.2025.
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Today the internet creates a new generation with modern culture that uses digital media. Social media is one of the popular digital media. Facebook is one of the social media that is quite liked by young people. They are accustomed to conveying their thoughts and expression through social media. Text mining analysis can be used to classify one's personality through social media with the probabilistic neural network algorithm. The text can be taken from the status that is on Facebook. In this study, there are three stages, namely text processing, weighting, and probabilistic neural networks for determining classification. Text processing consists of several processes, namely: tokenization, stopword, and steaming. The results of the text processing in the form of text are given a weight value to each word by using the Term Inverse Document Frequent (TF / IDF) method. In the final stage, the Probabilistic Neural Network Algorithm is used to classify personalities. This study uses 25 respondents, with 10 data as training data, and 15 data as testing data. The results of this study reached an accuracy of 60%.
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Khairina, Nurul, and Muhammad Khoiruddin Harahap. "Literature Study: Highway Traffic Management with Sentiment Analysis and Data Mining." Brilliance: Research of Artificial Intelligence 1, no. 1 (September 16, 2021): 27–31. http://dx.doi.org/10.47709/brilliance.v1i1.1096.
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In today's era, technology is growing rapidly, many of the latest technologies are in great demand by the Indonesian people, one of which is social media. Various social media such as Facebook, Twitter, Instagram, have become very popular applications for various ages, including teenagers, adults, and the elderly. Social media has a positive impact that can help people convey the latest information through posts on their respective accounts. Social media can disseminate information in a short time, this is why social media is an interesting application to research. The problem of road traffic congestion is strongly influenced by the number of vehicles that pass every day. A large number of private vehicles and public vehicles that pass greatly confuses the atmosphere of highway traffic. Congestion often occurs during working hours. Road congestion also often occurs when an unwanted incident occurs. Sentiment analysis algorithms and data mining algorithms can be combined to find information on traffic jams through social media such as Facebook, Twitter, Instagram, and other social media. The results show that sentiment analysis methods and data mining algorithms can be used to find information about current traffic jams through social media. The conclusion from this literature study can be seen that the K-Nearest Neighbor data mining algorithm is the best choice to overcome road traffic congestion, which will then be further developed in the form of highway traffic management modeling.
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Min, Seong Jae. "From algorithmic disengagement to algorithmic activism: Charting social media users’ responses to news filtering algorithms." Telematics and Informatics 43 (October 2019): 101251. http://dx.doi.org/10.1016/j.tele.2019.101251.
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Yan, Wei Qi, Xiaotian Wu, and Feng Liu. "Progressive Scrambling for Social Media." International Journal of Digital Crime and Forensics 10, no. 2 (April 2018): 56–73. http://dx.doi.org/10.4018/ijdcf.2018040104.
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Despite research work achieving progress in preserving the privacy of user profiles and visual surveillance, correcting problems in social media have not taken a great step. The reason is the lack of effective modelling, computational algorithms, and resultant evaluations in quantitative research. In this article, the authors take social media into consideration and link users together under the umbrella of social networks so as to exploit a way that the potential problems related to media privacy could be solved. The author's contributions are to propose tensor product-based progressive scrambling approaches for privacy preservation of social media and apply our approaches to the given social media which may encapsulate privacy before being viewed so as to achieve the goal of privacy preservation in anonymity, diverse and closeness. These approaches fully preserve the media information of the scrambled image and make sure it is able to be restored. The results show the proposed privacy persevering approaches are effective and have outstanding performance in media privacy preservation.
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Soonthornphisaj, Nuanwan, Taratep Sira-Aksorn, and Pornchanok Suksankawanich. "Social Media Comment Management using SMOTE and Random Forest Algorithms." International Journal of Networked and Distributed Computing 6, no. 4 (2018): 204. http://dx.doi.org/10.2991/ijndc.2018.4.6.2.
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Soonthornphisaj, Nuanwan, Taratep Sira-Aksorn, and Pornchanok Suksankawanich. "Social Media Comment Management using SMOTE and Random Forest Algorithms." International Journal of Networked and Distributed Computing 6, no. 4 (2018): 204. http://dx.doi.org/10.2991/ijndc.2018.6.4.2.
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Ragupathy, R., and Lakshmana Phaneendra Maguluri. "Comparative analysis of machine learning algorithms on social media test." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 284. http://dx.doi.org/10.14419/ijet.v7i2.8.10425.
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Sentiment analysis deals with identifying and classifying opinions or sentiments expressed in main text. It mainly refers to a text classification. Social media is generating a vast amount of sentiment rich data in the form of tweets, blog posts, comments, status updates, news etc. Sentiment analysis of this user generated data is very useful in knowing the opinion of the public. Knowledge base approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. In this paper, Machine learning approach has been used for the sentiment analysis of movie review dataset and is analysed by Naïve Bayes, Decision tree, KNN, and SVM classifiers. Commencing the most efficient classification technique is the moto of the paper. Efficiency of the classifier is decided based on some regular parameters that are outputs of the classification techniques.
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Dokuz, A. S., and M. Celik. "FAST SS-ILM: A COMPUTATIONALLY EFFICIENT ALGORITHM TO DISCOVER SOCIALLY IMPORTANT LOCATIONS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 197–202. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-197-2017.
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Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provide several valuable information about user behaviours on social media networking sites. However, discovering socially important locations are challenging due to data volume and dimensions, spatial and temporal calculations, location sparseness in social media datasets, and inefficiency of current algorithms. In the literature, several studies are conducted to discover important locations, however, the proposed approaches do not work in computationally efficient manner. In this study, we propose Fast SS-ILM algorithm by modifying the algorithm of SS-ILM to mine socially important locations efficiently. Experimental results show that proposed Fast SS-ILM algorithm decreases execution time of socially important locations discovery process up to 20 %.
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Bahrawi, Nfn. "Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based." Journal of Information Technology and Its Utilization 2, no. 2 (December 19, 2019): 29. http://dx.doi.org/10.30818/jitu.2.2.2695.
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Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. Keywords: sentiment analysis; random forest algorithm; clasification; machine learnings.
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Elgazzar, Heba, Kyle Spurlock, and Tanner Bogart. "Evolutionary clustering and community detection algorithms for social media health surveillance." Machine Learning with Applications 6 (December 2021): 100084. http://dx.doi.org/10.1016/j.mlwa.2021.100084.
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Alharbi, Manal S. F., and El-Sayed M. El-kenawy. "Optimize Machine Learning Programming Algorithms for Sentiment Analysis in Social Media." International Journal of Computer Applications 174, no. 25 (March 18, 2021): 38–43. http://dx.doi.org/10.5120/ijca2021921169.
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Just, Natascha, and Michael Latzer. "Governance by algorithms: reality construction by algorithmic selection on the Internet." Media, Culture & Society 39, no. 2 (July 9, 2016): 238–58. http://dx.doi.org/10.1177/0163443716643157.
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This article explores the governance by algorithms in information societies. Theoretically, it builds on (co-)evolutionary innovation studies in order to adequately grasp the interplay of technological and societal change and combines these with institutional approaches to incorporate governance by technology or rather software as institutions. Methodologically, it draws from an empirical survey of Internet-based services that rely on automated algorithmic selection, a functional typology derived from it, and an analysis of associated potential social risks. It shows how algorithmic selection has become a growing source of social order, of a shared social reality in information societies. It argues that – similar to the construction of realities by traditional mass media – automated algorithmic selection applications shape daily lives and realities, affect the perception of the world, and influence behavior. However, the co-evolutionary perspective on algorithms as institutions, ideologies, intermediaries, and actors highlights differences that are to be found, first, in the growing personalization of constructed realities and, second, in the constellation of involved actors. Altogether, compared to reality construction by traditional mass media, algorithmic reality construction tends to increase individualization, commercialization, inequalities, and deterritorialization and to decrease transparency, controllability, and predictability.
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Wang, Zhu, Liu, and Wang. "Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect." Future Internet 11, no. 4 (April 11, 2019): 95. http://dx.doi.org/10.3390/fi11040095.
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Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions.
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Thorson, Kjerstin. "Attracting the news: Algorithms, platforms, and reframing incidental exposure." Journalism 21, no. 8 (May 11, 2020): 1067–82. http://dx.doi.org/10.1177/1464884920915352.
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This article argues for new approaches to the study of incidental exposure that better account for the role of algorithms, platforms, and processes of datafication in shaping the likelihood of news exposure online. It offers a critique of three themes prominent in the incidental exposure literature: (1) incidental exposure connotes accidental exposure to news on social media, (2) news content is ubiquitous on social media, and (3) incidental exposure can be conceptually distinguished from intentional exposure to news on social media. This article proposes a new metaphor to reframe research on incidental exposure: ‘attraction’ to news.
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Swart, Joëlle. "Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media." Social Media + Society 7, no. 2 (April 2021): 205630512110088. http://dx.doi.org/10.1177/20563051211008828.
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The news that young people consume is increasingly subject to algorithmic curation. Yet, while numerous studies explore how algorithms exert power in citizens’ everyday life, little is known about how young people themselves perceive, learn about, and deal with news personalization. Considering the interactions between algorithms and users from an user-centric perspective, this article explores how young people make sense of, feel about, and engage with algorithmic news curation on social media and when such everyday experiences contribute to their algorithmic literacy. Employing in-depth interviews in combination with the walk-through method and think-aloud protocols with a diverse group of 22 young people aged 16–26 years, it addresses three current methodological challenges to studying algorithmic literacy: first, the lack of an established baseline about how algorithms operate; second, the opacity of algorithms within everyday media use; and third, limitations in technological vocabularies that hinder young people in articulating their algorithmic encounters. It finds that users’ sense-making strategies of algorithms are context-specific, triggered by expectancy violations and explicit personalization cues. However, young people’s intuitive and experience-based insights into news personalization do not automatically enable young people to verbalize these, nor does having knowledge about algorithms necessarily stimulate users to intervene in algorithmic decisions.
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Tuomchomtam, Sarach, and Nuanwan Soonthornphisaj. "Demographics and Personality Discovery on Social Media: A Machine Learning Approach." Information 12, no. 9 (August 30, 2021): 353. http://dx.doi.org/10.3390/info12090353.
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This research proposes a new feature extraction algorithm using aggregated user engagements on social media in order to achieve demographics and personality discovery tasks. Our proposed framework can discover seven essential attributes, including gender identity, age group, residential area, education level, political affiliation, religious belief, and personality type. Multiple feature sets are developed, including comment text, community activity, and hybrid features. Various machine learning algorithms are explored, such as support vector machines, random forest, multi-layer perceptron, and naïve Bayes. An empirical analysis is performed on various aspects, including correctness, robustness, training time, and the class imbalance problem. We obtained the highest prediction performance by using our proposed feature extraction algorithm. The result on personality type prediction was 87.18%. For the demographic attribute prediction task, our feature sets also outperformed the baseline at 98.1% for residential area, 94.7% for education level, 92.1% for gender identity, 91.5% for political affiliation, 60.6% for religious belief, and 52.0% for the age group. Moreover, this paper provides the guideline for the choice of classifiers with appropriate feature sets.
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Kosaraju, Naren Kumar, and Vineela Kanakamedala. "FRIEND RECOMMENDATION USING GRAPH MINING ON SOCIAL MEDIA." International Journal of Engineering Technology and Management Sciences 4, no. 5 (September 28, 2020): 57–65. http://dx.doi.org/10.46647/ijetms.2020.v04i05.011.
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Recommendation system is an important type of machine learning algorithm that provide precise suggestions to the users. Recommendation systems are used in innumerable types of areas such as generation of playlists, music and video services like Jio savaan, wynk, amazon prime music etc., and products recommendation for users in e-commerce applications and commercial applications. The recommendations that are provided by various types of applications increases the speed for identifying and makes easier to access the products that users are interested in. For each user, the recommendation system is capable of envisaging the future predilections on a set of items and recommend the top items. In several industries, recommendation systems are very useful as they generate huge amount of income and this type of industries can stand uniquely from competitors. Due to cumbersome number of items that each user can find in the web, the impact of recommendation system has been increased in the internet. Recommendation systems are used for custom-made navigation by getting huge amount of data particularly in social media domain for recommending friends. A recommendation system act as a subclass for the information filtering system that pursue to predict the rating. The similarity measures that are calculated in this research are Jaccard distance and Otsuka-Ochiai coefficient. The feature extractions that are used in this paper are Adar index, PageRank, Katz centrality, Hits score. Now a days many research people are implementing different types of algorithms in various domains for recommendation systems.
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Qin, Yufan Sunny. "Fostering brand–consumer interactions in social media: the role of social media uses and gratifications." Journal of Research in Interactive Marketing 14, no. 3 (September 4, 2020): 337–54. http://dx.doi.org/10.1108/jrim-08-2019-0138.
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Purpose An increasing number of brands are using algorithms to embed brand-related content to targeted consumers’ social media pages. This paper aims to analyze how do consumers’ motives of using social media in general influence their potential brand–consumer interactions and the following branding outcomes. To examine this, this study selected Facebook as the social media platform and Nike as the brand to conduct an online survey experiment to examine the effects of social media usage motives on consumers’ interactions with the brand in social media. Design/methodology/approach An online survey experiment using Nike’s Facebook page as the stimuli was conducted to analyze the interactions between consumers and a specific brand’s social media page in a natural setting. Data were collected in the USA via Amazon’s Mechanical Turk (MTurk). Findings This study demonstrated that brand–consumer interactions, both content-consumption and content-contribution intentions, can be fostered by certain motives of using social media: information-seeking and self-identity. This study also suggested that content-consumption behavior has significant associations with consumers’ positive attitudes toward the brand’s social media pages, while content-contribution behavior does not show significant effects. Originality/value This study provides new insights about how consumers’ general motives of social media usage influence their intentions to interact with the brand in social media from two levels (i.e. content-consumption and content-contribution).
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Mastykash, Oleh, and Andii Peleshchyshyn. "General algorithm for searching user data in social media of the Internet." European Journal of Engineering Research and Science 5, no. 1 (January 16, 2020): 82–86. http://dx.doi.org/10.24018/ejers.2020.5.1.1708.
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The research work presented within this paper solves the problem of automated search for heterogeneous data in social media of the Internet (SMI). Building a system for obtaining and subsequent analysis of heterogeneous data in SMI – a complex multi-stage process in which specialists of various profiles and qualifications participate. Therefore, one of the main problems in the design of such systems is the coverage of all aspects of the functioning of the software-analytical complex, providing a common language for specialists, which allows us to uniquely, and clearly, understandably formulate the basic concepts of the projects. One of the main and basic tasks in analyzing the pages of a SMI user is to build algorithms for analyzing the user data environment (UDE). The quality of software will depend on the implemented algorithms. The construction of such algorithms, on the one hand, provides an understanding of the process of forming functional individual modules of the system and their interaction, on the other hand, laying a qualitative foundation in the future system. Algorithms for data analysis in the SMI will be designed based on the basic principles of behavior of the user registered in it.
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Cetina Presuel, Rodrigo, and José Manuel Martínez Sierra. "Algorithms and the News: Social Media Platforms as News Publishers and Distributors." Revista de Comunicación 18, no. 2 (August 26, 2019): 261–85. http://dx.doi.org/10.26441/rc18.2-2019-a13.
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With billons of users, social media platforms (e.g. Facebook) are dominant players in a highly-concentrated online news market. They have great power over the distribution of information to their users, and over the organizations and individuals that produce it. Social media platforms use algorithms to perform functions traditionally belonging to news editors: deciding on the importance of news items and how they are disseminated. However, they do not acknowledge the role they play in informing the public as traditional news media always have and tend to ignore that they also act as publishers of news and the responsibilities associated with that role. This paper argues that it is essential for social media platforms to understand and embrace their role as both news publishers and distributors and highlights the essential responsibilities they must undertake so they can satisfy the information needs of their audiences and protect the public’s right to information.
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Guellil, Imane, Ahsan Adeel, Faical Azouaou, Sara Chennoufi, Hanene Maafi, and Thinhinane Hamitouche. "Detecting hate speech against politicians in Arabic community on social media." International Journal of Web Information Systems 16, no. 3 (July 31, 2020): 295–313. http://dx.doi.org/10.1108/ijwis-08-2019-0036.
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Purpose This paper aims to propose an approach for hate speech detection against politicians in Arabic community on social media (e.g. Youtube). In the literature, similar works have been presented for other languages such as English. However, to the best of the authors’ knowledge, not much work has been conducted in the Arabic language. Design/methodology/approach This approach uses both classical algorithms of classification and deep learning algorithms. For the classical algorithms, the authors use Gaussian NB (GNB), Logistic Regression (LR), Random Forest (RF), SGD Classifier (SGD) and Linear SVC (LSVC). For the deep learning classification, four different algorithms (convolutional neural network (CNN), multilayer perceptron (MLP), long- or short-term memory (LSTM) and bi-directional long- or short-term memory (Bi-LSTM) are applied. For extracting features, the authors use both Word2vec and FastText with their two implementations, namely, Skip Gram (SG) and Continuous Bag of Word (CBOW). Findings Simulation results demonstrate the best performance of LSVC, BiLSTM and MLP achieving an accuracy up to 91%, when it is associated to SG model. The results are also shown that the classification that has been done on balanced corpus are more accurate than those done on unbalanced corpus. Originality/value The principal originality of this paper is to construct a new hate speech corpus (Arabic_fr_en) which was annotated by three different annotators. This corpus contains the three languages used by Arabic people being Arabic, French and English. For Arabic, the corpus contains both script Arabic and Arabizi (i.e. Arabic words written with Latin letters). Another originality is to rely on both shallow and deep leaning classification by using different model for extraction features such as Word2vec and FastText with their two implementation SG and CBOW.
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Wagenpfeil, Stefan, Felix Engel, Paul Mc Kevitt, and Matthias Hemmje. "AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones." Information 12, no. 1 (January 19, 2021): 43. http://dx.doi.org/10.3390/info12010043.
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To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones.
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Cinelli, Matteo, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini. "The echo chamber effect on social media." Proceedings of the National Academy of Sciences 118, no. 9 (February 23, 2021): e2023301118. http://dx.doi.org/10.1073/pnas.2023301118.
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Social media may limit the exposure to diverse perspectives and favor the formation of groups of like-minded users framing and reinforcing a shared narrative, that is, echo chambers. However, the interaction paradigms among users and feed algorithms greatly vary across social media platforms. This paper explores the key differences between the main social media platforms and how they are likely to influence information spreading and echo chambers’ formation. We perform a comparative analysis of more than 100 million pieces of content concerning several controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. We quantify echo chambers over social media by two main ingredients: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter. We conclude the paper by directly comparing news consumption on Facebook and Reddit, finding higher segregation on Facebook.
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Peterson-Salahuddin, Chelsea, and Nicholas Diakopoulos. "Negotiated Autonomy: The Role of Social Media Algorithms in Editorial Decision Making." Media and Communication 8, no. 3 (July 10, 2020): 27–38. http://dx.doi.org/10.17645/mac.v8i3.3001.
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Social media platforms have increasingly become an important way for news organizations to distribute content to their audiences. As news organizations relinquish control over distribution, they may feel the need to optimize their content to align with platform logics to ensure economic sustainability. However, the opaque and often proprietary nature of platform algorithms makes it hard for news organizations to truly know what kinds of content are preferred and will perform well. Invoking the concept of algorithmic ‘folk theories,’ this article presents a study of in-depth, semi-structured interviews with 18 U.S.-based news journalists and editors to understand how they make sense of social media algorithms, and to what extent this influences editorial decision making. Our findings suggest that while journalists’ understandings of platform algorithms create new considerations for gatekeeping practices, the extent to which it influences those practices is often negotiated against traditional journalistic conceptions of newsworthiness and journalistic autonomy.
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Couldry, Nick, Clemencia Rodriguez, Göran Bolin, Julie Cohen, Ingrid Volkmer, Gerard Goggin, Marwan Kraidy, et al. "Media, communication and the struggle for social progress." Global Media and Communication 14, no. 2 (June 11, 2018): 173–91. http://dx.doi.org/10.1177/1742766518776679.
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This article discusses the role of media and communications in contributing to social progress, as elaborated in a landmark international project – the International Panel on Social Progress. First, it analyses how media and digital platforms have contributed to global inequality by examining media access and infrastructure across world regions. Second, it looks at media governance and the different mechanisms of corporatized control over media platforms, algorithms and content. Third, the article examines how the democratization of media is a key element in the struggle for social justice. It argues that effective media access – in terms of distribution of media resources, even relations between spaces of connection and the design and operation of spaces that foster dialogue, free speech and respectful cultural exchange – is a core component of social progress.
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Levy, Ro’ee. "Social Media, News Consumption, and Polarization: Evidence from a Field Experiment." American Economic Review 111, no. 3 (March 1, 2021): 831–70. http://dx.doi.org/10.1257/aer.20191777.
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Does the consumption of ideologically congruent news on social media exacerbate polarization? I estimate the effects of social media news exposure by conducting a large field experiment randomly offering participants subscriptions to conservative or liberal news outlets on Facebook. I collect data on the causal chain of media effects: subscriptions to outlets, exposure to news on Facebook, visits to online news sites, and sharing of posts, as well as changes in political opinions and attitudes. Four main findings emerge. First, random variation in exposure to news on social media substantially affects the slant of news sites that individuals visit. Second, exposure to counter-attitudinal news decreases negative attitudes toward the opposing political party. Third, in contrast to the effect on attitudes, I find no evidence that the political leanings of news outlets affect political opinions. Fourth, Facebook’s algorithm is less likely to supply individuals with posts from counter-attitudinal outlets, conditional on individuals subscribing to them. Together, the results suggest that social media algorithms may limit exposure to counter-attitudinal news and thus increase polarization. (JEL C93, D72, L82)
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Ebrahimpour, Zeinab, Wanggen Wan, José Luis Velázquez García, Ofelia Cervantes, and Li Hou. "Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data." ISPRS International Journal of Geo-Information 9, no. 2 (February 21, 2020): 125. http://dx.doi.org/10.3390/ijgi9020125.
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Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens.
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Panigrahi, Rasmita, Neelamdhab Padhy, and Suresh Chandra Satapathy. "Software Reusability Metrics Estimation From the Social Media by Using Evolutionary Algorithms." International Journal of Open Source Software and Processes 10, no. 2 (April 2019): 21–36. http://dx.doi.org/10.4018/ijossp.2019040102.
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Software expansion is rising with the help of the standard paradigm in the 21st century. The maximum contribution of software growth focuses mainly on object-oriented development methodologies. This paradigm helps the developer to develop code quickly and makes sure that the platform assists in producing a quality product. The software reusability metrics play a crucial role for software development. To overcome the scalability issues, researchers and developers both adopt a CK metrics suite to extract the software metrics to extract the features from the repositories. The main objective of this article is to extract the set of metrics from social media by using novel evolutionary techniques. Dissimilar features within this area are examined with a suitable research query that discovers the potential and extent.
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Ozbay, Feyza Altunbey, and Bilal Alatas. "Fake news detection within online social media using supervised artificial intelligence algorithms." Physica A: Statistical Mechanics and its Applications 540 (February 2020): 123174. http://dx.doi.org/10.1016/j.physa.2019.123174.
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Et. al., Rushali Deshmukh,. "Performance Comparison for Spam Detection in Social Media Using Deep Learning Algorithms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 193–201. http://dx.doi.org/10.17762/turcomat.v12i1s.1609.
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Social media applications like Twitter, Instagram, Facebook have helped people to connect to each other. This has been eased due to high-speed internet. However, this has invited various spam messages through tweets or Facebook. The sole purpose of such messages is aggregation or exploitation of personal data in terms of finances or medical records, political benefit’s or community violence. This makes spam detection an extreme value-added service. We tend to recommend a 1D CNN algorithmic technique and compare results with variants of CNN and with boosting algorithms. The model is braced with linguistics data in the illustration of the words with the assistance of knowledge-bases such as Word2vec and fast ext. This improves the end to end performance, by providing higher linguistics vector illustration of input testing words. Projected Experimental results show the efficiency of the projected approach from the point of view of accuracy, F1-score and response time.
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Mutanov, Galimkair, Vladislav Karyukin, and Zhanl Mamykova. "Multi-Class Sentiment Analysis of Social Media Data with Machine Learning Algorithms." Computers, Materials & Continua 69, no. 1 (2021): 913–30. http://dx.doi.org/10.32604/cmc.2021.017827.
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Reviglio, Urbano, and Claudio Agosti. "Thinking Outside the Black-Box: The Case for “Algorithmic Sovereignty” in Social Media." Social Media + Society 6, no. 2 (April 2020): 205630512091561. http://dx.doi.org/10.1177/2056305120915613.
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This article is an interdisciplinary critical analysis of personalization systems and the gatekeeping role of current mainstream social media. The first section presents a literature review of data-driven personalization and its challenges in social media. The second section sheds light on increasing concerns regarding algorithms’ ability to overtly persuade—and covertly manipulate—users for the sake of engagement, introducing the emergence of the exclusive ownership of behavioral modification through hyper-nudging techniques. The third section empirically analyzes users’ expectations and behaviors regarding such data-driven personalization to frame a conceptualization of users’ agency. The fourth section introduces the concept of “algorithmic sovereignty.” Current projects that aim to grant this algorithmic sovereignty highlight some potential applications. Together this novel theoretical framework and empirical applications suggest that, to preserve trust, social media should open their personalization algorithms to a social negotiation as the first step toward a more sustainable social media landscape. To decentralize the immense power of mainstream social media, guarantee a democratic oversight, and mitigate the unintended undesirable consequences of their algorithmic curation, public institutions and civil society could help in developing and researching public algorithms, fostering a collective awareness so as to eventually ensure a fair and accountable “algorithmic sovereignty.”
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Basarslan, Muhammet Sinan, and Fatih Kayaalp. "Sentiment Analysis with Machine Learning Methods on Social Media." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 9, no. 3 (September 17, 2020): 5–15. http://dx.doi.org/10.14201/adcaij202093515.
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Social media has become an important part of our everyday life due to the widespread use of the Internet. Of the social media services, Twitter is among the most used ones around the world. People share their opinions by writing tweets about numerous subjects, such as politics, sports, economy, etc. Millions of tweets per day create a huge dataset, which drew attention of the data scientists to focus on these data for sentiment analysis. The sentiment analysis focuses to identify the social media posts of users about a specific topic and categorize them as positive, negative or neutral. Thus, the study aims to investigate the effect of types of text representation on the performance of sentiment analysis. In this study, two datasets were used in the experiments. The first one is the user reviews about movies from the IMDB, which has been labeled by Kotzias, and the second one is the Twitter tweets, including the tweets of users about health topic in English in 2019, collected using the Twitter API. The Python programming language was used in the study both for implementing the classification models using the Naïve Bayes (NB), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) algorithms, and for categorizing the sentiments as positive, negative and neutral. The feature extraction from the dataset was performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec (W2V) modeling techniques. The success percentages of the classification algorithms were compared at the end. According to the experimental results, Artificial Neural Network had the best accuracy performance in both datasets compared to the others.
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Song, Yi, Xuesong Lu, Sadegh Nobari, Stéphane Bressan, and Panagiotis Karras. "On the Privacy and Utility of Anonymized Social Networks." International Journal of Adaptive, Resilient and Autonomic Systems 4, no. 2 (April 2013): 1–34. http://dx.doi.org/10.4018/jaras.2013040101.
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One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.
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N.V.G. Sirisha, G., G. V.Padma Raju, and G. Amruta. "Spam Detection on Online Social Media Networks." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 631. http://dx.doi.org/10.14419/ijet.v7i2.7.10896.
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Now-a-days people are generally using social networking sites for communicating with the other users and for sharing information across the world. The online social networking sites are becoming the significant tools and are providing a common medium for number of users to communicate with each other. The large amount of information that is accessible on the social networking sites retain the cyber attackers, who generally exploit the information available for their benefits. They generally infect the user’s system, appeal the victims to click on malicious links, advertise some products only to gain money. Spam profiles are becoming major security threat used by cyber criminals and also a source of unwanted ads. Twitter is one among several social networking sites which are expanding on daily basis. Spam detection in twitter has become one of the major problems these days. A twitter spam account user nature is analyzed with a target to improve detection of social spam. An innovative technique based on deep learning technology is used for the identification of spam accounts in twitter. These techniques have an advantage that they use raw data to learn high level features on their own, unlike the traditional machine learning algorithms which require native features for the application of classification model.
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Boccia Artieri, Giovanni, and Laura Gemini. "Mass media and the web in the light of Luhmann’s media system." Current Sociology 67, no. 4 (April 8, 2019): 563–78. http://dx.doi.org/10.1177/0011392119837542.
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The aim of the article is to observe the contemporary media system in the light of Luhmann’s media system, namely a specific function system of society which has witnessed ever greater internal complexity vis-a-vis an environment marked by the spread of the web and social network sites. From the viewpoint of sociocybernetics, the question of increased complexity can be addressed through an ecological approach in order to analyse the distinction between the mass media and the web – in its specific 2.0 evolution, characterized by user-generated content and algorithms. This approach allows to observe the reciprocal relations by preserving the autonomy of the two spheres without resorting to explanations that have to do with hybridization or the blur of the boundaries. In this sense the article analyses Facebook – as an example of web 2.0 operational logic – as a social system distinct from that of the mass media, where the first substantial difference depends on the role played by individuals in reproducing communication and on the role of the algorithm. In this sense mass media and the web are treated on the basis of their relationship of structural coupling by observing how they irritate, or disturb, each other and at the same time maintain their autonomy.
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Balicki, Jerzy, Honorata Balicka, Piotr Dryja, and Maciej Tyszka. "Social media for e-learning of citizens in smart city." SHS Web of Conferences 57 (2018): 01002. http://dx.doi.org/10.1051/shsconf/20185701002.
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The rapid development of social media can be applied for citizens’ e-learning in a smart city. Big cities have to cope with several open issues like a growing population or a traffic congestion. Especially, a home and public space is supposed to be used in more efficient way. Sustainable homes and buildings can be planned with using some modern techniques. Even currently, there is a huge problem with a lack of key resources like water and energy. So, an efficient management of resources is strongly required. What is more, a global warming and carbon emissions are considered as some critical factors for living conditions in many cities, too. A vision of a smart city is related to a better protection of a natural environment and a more efficient use of it. Moreover, citizens expect an efficient and sustainable transportation in livable city. To present some solutions on above issues, this paper outlines the methodology of using social media to provide necessary knowledge by citizenship training systems. In particular, some selected e-learning applications have been characterized. They are related to pedagogical agents in e-learning. In addition, some advanced meta-heuristics have been proposed with particular emphasis on genetic programming, artificial neural networks, neuro-evolution algorithms, support vector machines, and some collective intelligence algorithms. Finally, cloud services are discussed regarding a smart city management and training.
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Ladeiras-Lopes, Ricardo, Lavinia Baciu, Julia Grapsa, Afzal Sohaib, Rafael Vidal-Perez, Allan Bohm, Harri Silvola, et al. "Social media in cardiovascular medicine: a contemporary review." European Heart Journal - Digital Health 1, no. 1 (November 1, 2020): 10–19. http://dx.doi.org/10.1093/ehjdh/ztaa004.
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Abstract Social media increasingly impact both the private and professional lives of the majority of the population, including individuals engaged in cardiovascular healthcare and research. Healthcare providers across the world use social media platforms such as Twitter or Facebook to find medical and scientific information, to follow scientific meetings, to discuss individual clinical cases with colleagues, and to engage with patients. While social media provide a means for fast, interactive and accessible communication without geographic boundaries, their use to obtain and disseminate information has limitations and the potential threats are not always clearly understood. Governance concerns include a lack of rigorous quality control, bias due to the pre-selection of presented content by filter algorithms, and the risk of inadvertent breach of patient confidentiality. This article provides information and guidance regarding the role and use of social media platforms in cardiovascular medicine, with an emphasis on the new opportunities for the dissemination of scientific information and continuing education that arise from their responsible use.