Bubbles Bursting: Investigating and Measuring the Personalisation of Social Media Searches

[1]  M. Tanvir,et al.  Islamophobia: Causes and countermeasures , 2023, Asian Journal of Comparative Politics.

[2]  O. Shcherbakova,et al.  SOCIAL MEDIA AND FILTER BUBBLES , 2022, Scientific Journal of Polonia University.

[3]  David M. Bourrie,et al.  Integrating truth bias and elaboration likelihood to understand how political polarisation impacts disinformation engagement on social media , 2022, Inf. Syst. J..

[4]  U. Sivarajah,et al.  How Does Misinformation and Capricious Opinions Impact the Supply Chain - A Study on the Impacts During the Pandemic , 2022, Annals of Operations Research.

[5]  Remo Gramigna.  Inside Facebook’s semiosphere. How social media influence digital hate and fuel cyber-polarization , 2022, Social Semiotics.

[6]  Filipo Sharevski,et al.  (Mis)perceptions and engagement on Twitter: COVID-19 vaccine rumors on efficacy and mass immunization effort , 2021, International Journal of Information Management Data Insights.

[7]  Bernardo Pereira Nunes,et al.  The BiasChecker: how biased are social media searches? , 2021, ASONAM.

[8]  Saptarsi Goswami,et al.  Combating the menace: A survey on characterization and detection of fake news from a data science perspective , 2021, Int. J. Inf. Manag. Data Insights.

[9]  Luca Belli,et al.  Algorithmic amplification of politics on Twitter , 2021, Proceedings of the National Academy of Sciences.

[10]  Dustin Carnahan,et al.  Flooding the Zone: How Exposure to Implausible Statements Shapes Subsequent Belief Judgments , 2021, International Journal of Public Opinion Research.

[11]  Yezheng Liu,et al.  Prick the filter bubble: A novel cross domain recommendation model with adaptive diversity regularization , 2021, Electronic Markets.

[12]  A. Kar,et al.  How to differentiate propagators of information and misinformation–Insights from social media analytics based on bio-inspired computing , 2021, Journal of Information and Optimization Sciences.

[13]  Naresh Kumar Agarwal,et al.  Creation, dissemination and mitigation: toward a disinformation behavior framework and model , 2021, Aslib J. Inf. Manag..

[14]  J. V. Van Bavel,et al.  How social media shapes polarization , 2021, Trends in Cognitive Sciences.

[15]  Feiyu,et al.  Comparison between Calculation Methods for Semantic Text Similarity based on Siamese Networks , 2021, DSIT.

[16]  Andrés Abeliuk,et al.  Auditing Algorithmic Bias on Twitter , 2021, WebSci.

[17]  Eriyanto Eriyanto,et al.  Political Polarization and Selective Exposure of Social Media Users in Indonesia , 2021 .

[18]  A. Ozdaglar,et al.  Misinformation: Strategic Sharing, Homophily, and Endogenous Echo Chambers , 2021, SSRN Electronic Journal.

[19]  Stefan M. Herzog,et al.  Public attitudes towards algorithmic personalization and use of personal data online: evidence from Germany, Great Britain, and the United States , 2021, Humanities and Social Sciences Communications.

[20]  Samuel C. Rhodes.  Filter Bubbles, Echo Chambers, and Fake News: How Social Media Conditions Individuals to Be Less Critical of Political Misinformation , 2021, Political Communication.

[21]  Jack Bandy,et al.  Problematic Machine Behavior , 2021, Proc. ACM Hum. Comput. Interact..

[22]  Nicholas Diakopoulos,et al.  More Accounts, Fewer Links , 2021, Proc. ACM Hum. Comput. Interact..

[23]  Dickson K. W. Chiu,et al.  The Role of Online Misinformation and Fake News in Ideological Polarization: Barriers, Catalysts, and Implications , 2021, Information Systems Frontiers.

[24]  J. Baptista,et al.  “Brave New World” of Fake News: How It Works , 2021, Javnost - The Public.

[25]  Joëlle Swart.  Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media , 2021, Social Media + Society.

[26]  James A. Piazza.  Fake news: the effects of social media disinformation on domestic terrorism , 2021, Dynamics of Asymmetric Conflict.

[27]  M. Glymour,et al.  Evaluating associations between area-level Twitter-expressed negative racial sentiment, hate crimes, and residents' racial prejudice in the United States , 2021, SSM - population health.

[28]  Hyunyi Cho,et al.  Testing three explanations for stigmatization of people of Asian descent during COVID-19: maladaptive coping, biased media use, or racial prejudice? , 2020, Ethnicity & health.

[29]  Yogesh K. Dwivedi,et al.  Theory building with big data-driven research - Moving away from the "What" towards the "Why" , 2020, Int. J. Inf. Manag..

[30]  Ro’ee Levy.  Social Media, News Consumption, and Polarization: Evidence from a Field Experiment , 2020, American Economic Review.

[31]  Jonatas C. dos Santos,et al.  Is There Personalization in Twitter Search? A Study on polarized opinions about the Brazilian Welfare Reform , 2020, 12th ACM Conference on Web Science.

[32]  A. Kar,et al.  Experience: Managing Misinformation in Social Media - Insights for Policymakers from Twitter Analytics , 2020, ACM J. Data Inf. Qual..

[33]  Muyang Li,et al.  The ‘bad women drivers’ myth: the overrepresentation of female drivers and gender bias in China’s media , 2020 .

[34]  Derek McAuley,et al.  The impact of algorithmic decision-making processes on young people’s well-being , 2019, Health Informatics J..

[35]  Ray Kurzweil,et al.  Multilingual Universal Sentence Encoder for Semantic Retrieval , 2019, ACL.

[36]  T. Lee.  The global rise of “fake news” and the threat to democratic elections in the USA , 2019, Public Administration and Policy.

[37]  Zubair Shafiq,et al.  Measuring Political Personalization of Google News Search , 2019, WWW.

[38]  Ana-Andreea Stoica,et al.  Hegemony in Social Media and the effect of recommendations , 2019, WWW.

[39]  N. Perra,et al.  Modelling opinion dynamics in the age of algorithmic personalisation , 2018, Scientific Reports.

[40]  G. Mark Grimes,et al.  Supporting Better Decisions: How Order Effects Influence Decision Support System Alignment , 2018, Interact. Comput..

[41]  Krishna P. Gummadi,et al.  Search bias quantification: investigating political bias in social media and web search , 2018, Information Retrieval Journal.

[42]  Eitan Altman,et al.  Biases in the Facebook News Feed: A Case Study on the Italian Elections , 2018, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[43]  David Lazer,et al.  Auditing the Personalization and Composition of Politically-Related Search Engine Results Pages , 2018, WWW.

[44]  Murali Mani,et al.  Effective Big Data Visualization , 2017, IDEAS.

[45]  Virgílio A. F. Almeida,et al.  "Everything I Disagree With is #FakeNews": Correlating Political Polarization and Spread of Misinformation , 2017, ArXiv.

[46]  Junyong In,et al.  Statistical data presentation , 2017, Korean journal of anesthesiology.

[47]  A.-Reum Jung,et al.  The influence of perceived ad relevance on social media advertising: An empirical examination of a mediating role of privacy concern , 2017, Comput. Hum. Behav..

[48]  Alamir Novin,et al.  Making Sense of Conflicting Science Information: Exploring Bias in the Search Engine Result Page , 2017, CHIIR.

[49]  Harald Holone,et al.  The filter bubble and its effect on online personal health information , 2016, Croatian medical journal.

[50]  Carmen Aguilera-Carnerero,et al.  ‘Islamonausea, not Islamophobia’: The many faces of cyber hate speech , 2016 .

[51]  Damian Trilling,et al.  Should We Worry About Filter Bubbles? , 2016 .

[52]  Tom Rodden,et al.  Privacy concerns arising from internet service personalization filters , 2016, SIGCAS Comput. Soc..

[53]  J. Hoven,et al.  Breaking the filter bubble: democracy and design , 2015, Ethics and Information Technology.

[54]  David Lazer,et al.  Location, Location, Location: The Impact of Geolocation on Web Search Personalization , 2015, Internet Measurement Conference.

[55]  Helen Ashman,et al.  Examining Personalization in Academic Web Search , 2015, HT.

[56]  Ronald E. Robertson,et al.  The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections , 2015, Proceedings of the National Academy of Sciences.

[57]  Lada A. Adamic,et al.  Exposure to ideologically diverse news and opinion on Facebook , 2015, Science.

[58]  Filippo Menczer,et al.  Measuring Online Social Bubbles , 2015, 1502.07162.

[59]  Julita Vassileva,et al.  Understanding and controlling the filter bubble through interactive visualization: a user study , 2014, HT.

[60]  Engin Bozdag.  Bias in algorithmic filtering and personalization , 2013, Ethics and Information Technology.

[61]  Balachander Krishnamurthy,et al.  Measuring personalization of web search , 2013, WWW.

[62]  Ron Kohavi,et al.  Trustworthy online controlled experiments: five puzzling outcomes explained , 2012, KDD.

[63]  Neil Thurman,et al.  THE FUTURE OF PERSONALIZATION AT NEWS WEBSITES , 2012 .

[64]  Ron Kohavi,et al.  Controlled experiments on the web: survey and practical guide , 2009, Data Mining and Knowledge Discovery.

[65]  Thorsten Joachims,et al.  In Google We Trust: Users' Decisions on Rank, Position, and Relevance , 2007, J. Comput. Mediat. Commun..

[66]  R. Hogarth,et al.  Order effects in belief updating: The belief-adjustment model , 1992, Cognitive Psychology.

[67]  S. Asch.  Forming impressions of personality. , 1946, Journal of abnormal psychology.

[68]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[69]  Derek McAuley,et al.  Editorial responsibilities arising from personalization algorithms , 2017 .

[70]  Karrie Karahalios,et al.  Auditing Algorithms : Research Methods for Detecting Discrimination on Internet Platforms , 2014 .

[71]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[72]  P. Jaccard.  Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines , 1901 .