Political communication on social media: A tale of hyperactive users and bias in recommender systems

J. Gottfried, E. Shearer, Americans’ online news use is closing in on tv news use, 2017, http://www.pewresearch.org/fact-tank/2017/09/07/americans-online-news-use-vs-tv-news-use/. Hegelich, 2015, The communication behavior of german MPS on twitter: preaching to the converted and attacking opponents, Eur. Policy Anal., 1, 155, 10.18278/epa.1.2.8 Enli, 2013, Personalized campaigns in party-centred politics: Twitter and Facebook as arenas for political communication, Inf. Commun. Soc., 16, 757, 10.1080/1369118X.2013.782330 Arnaboldi, 2017, Structure of ego-alter relationships of politicians in twitter, J. Comput. Mediated Commun., 22, 231, 10.1111/jcc4.12193 Serrano, 2018 Papakyriakopoulos, 2018, Social media and microtargeting: Political data processing and the consequences for germany, Big Data Soc., 5, 10.1177/2053951718811844 McCombs, 1972, The agenda-setting function of mass media, Public Opin. Q., 36, 176, 10.1086/267990 Bakshy, 2015, Exposure to ideologically diverse news and opinion on Facebook, Science, 348, 1130, 10.1126/science.aaa1160 Twitter, 2018, (https://help.twitter.com/en/using-twitter/twitter-trending-faqs). Online; accessed 24 August 2018. Hilbert, 2017, One step, two step, network step? Complementary perspectives on communication flows in twittered citizen protests, Soc. Sci. Comput. Rev., 35, 444, 10.1177/0894439316639561 Choi, 2015, The two-step flow of communication in twitter-based public forums, Soc. Sci. Comput. Rev., 33, 696, 10.1177/0894439314556599 Mouffe, 2000 N. Blenn, P. Van Mieghem, Are human interactivity times lognormal?, arXiv preprint (2016). Van Mieghem, 2011, Lognormal distribution in the digg online social network, Eur. Phys. J. B, 83, 251, 10.1140/epjb/e2011-20124-0 Lerman, 2010, Information contagion: An empirical study of the spread of news on digg and twitter social networks, 90 Benevenuto, 2009, Characterizing user behavior in online social networks, 49 Schaap, 2006, Agonism in divided societies, Philos. Soc. Crit., 32, 255, 10.1177/0191453706061095 Shi, 2010, The continuance of online social networks: how to keep people using Facebook?, 1 Sunstein, 2018 N. Analytics, The 2019 guide to Facebook publishing, http://go.newswhip.com/2019_03-FacebookPublishing_LP.html. Bucher, 2012, Want to be on the top? Algorithmic power and the threat of invisibility on Facebook, New Media Soc., 14, 1164, 10.1177/1461444812440159 Hersh, 2015 Boldrini, 2018, Twitter and the press: an ego-centred analysis, 1471 Romero, 2011, Influence and passivity in social media, 18 Weeks, 2017, Online influence? Social media use, opinion leadership, and political persuasion, Int. J. Public Opin. Res., 29, 214 Arnaboldi, 2017, Online social networks and information diffusion: the role of ego networks, Online Soc. Netw. Media, 1, 44, 10.1016/j.osnem.2017.04.001 Priya, 2019, Where should one get news updates: Twitter or Reddit, Online Soc. Netw. Media, 9, 17, 10.1016/j.osnem.2018.11.001 Hegelich, 2016, Are social bots on twitter political actors? Empirical evidence from a ukrainian social Botnet, 1 J. Weedon, W. Nuland, A. Stamos, Information operations and Facebook, Retrieved from Facebook: https://fbnewsroomus.files.wordpress.com/2017/04/facebook-and-information-operations-v1.pdf (2017). Covington, 2016, Deep neural networks for Youtube recommendations, 191 The Facebook algorithm explained and how to work it, https://www.brandwatch.com/blog/the-facebook-algorithm-explained/. Yuan, 2009, Augmenting collaborative recommender by fusing explicit social relationships, 1 He, 2008, Learning from imbalanced data, IEEE Trans. Knowl. Data Eng., 9, 1263 Lin, 2017, Criticality in Formal Languages and Statistical Physics, Entropy, 19, 299 Krishnan, 2014, A methodology for learning, analyzing, and mitigating social influence bias in recommender systems, 137 Cosley, 2003, Is seeing believing?: How recommender system interfaces affect users’ opinions, 585 X. Chen, C. Liu, B. Li, K. Lu, D. Song, Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning, http://arxiv.org/abs/1712.05526 [Cs] (2017). (accessed December 19, 2019). Li, 2016, Data poisoning attacks on factorization-based collaborative filtering, 1885 Bojchevski, 2019, Adversarial attacks on node embeddings via graph poisoning, 695 D. Zügner, S. Günnemann, Adversarial Attacks on Graph Neural Networks via Meta Learning, http://arxiv.org/abs/1902.08412 [Cs, Stat] (2019). (accessed December 19, 2019). Facebook, Using the graph API – documentation, https://developers.facebook.com/docs/graph-api/using-graph-api/. Varol, 2017, Online human-bot interactions: detection, estimation, and characterization, 1 Serrano, 2019, The rise of Germany’s AFD: a social media analysis, 214 Ruths, 2014, Social media for large studies of behavior, Science, 346, 1063, 10.1126/science.346.6213.1063 Arnold, 2011, Nonparametric goodness-of-fit tests for discrete null distributions., R J., 3, 10.32614/RJ-2011-016 Vuong, 1989, Likelihood ratio tests for model selection and non-nested hypotheses, Econom. J. Econom. Soc., 57, 307 Clauset, 2009, Power-law distributions in empirical data, SIAM review, 51, 661, 10.1137/070710111 Barnett, 1974 Johnson, 1998 Ben-Gal, 2005, Outlier detection, 131 Hubert, 2008, Outlier detection for skewed data, J. Chemom. J. Chemom. Soc., 22, 235 Hubert, 2008, An adjusted boxplot for skewed distributions, Comput. Stat. Data Anal., 52, 5186, 10.1016/j.csda.2007.11.008 Brys, 2004, A robust measure of skewness, J. Comput. Gr. Stat., 13, 996, 10.1198/106186004X12632 Martin, 2015, More efficient topic modelling through a noun only approach, 111 Honnibal, 2015, An improved non-monotonic transition system for dependency parsing, 1373 Brants, 2004, Tiger: linguistic interpretation of a German corpus, Res. Lang. Comput., 2, 597, 10.1007/s11168-004-7431-3 Nothman, 2013, Learning multilingual named entity recognition from wikipedia, Artif. Intell., 194, 151, 10.1016/j.artint.2012.03.006 Yurochkin, 2017, Conic scan-and-cover algorithms for nonparametric topic modeling, 3878 Blei, 2003, Latent Dirichlet allocation, J. Mach. Learn. Res., 3, 993 Lee, 1999, Learning the parts of objects by non-negative matrix factorization, Nature, 401, 788, 10.1038/44565 Scholz, 1987, K-sample Anderson–darling tests, J. Am. Stat. Assoc., 82, 918 Leskovec, 2012, Learning to discover social circles in ego networks, 539 McPherson, 2001, Birds of a feather: homophily in social networks, Annu. Rev. Sociol., 27, 415, 10.1146/annurev.soc.27.1.415 J. Kirk, Tensorrec: a tensorflow recommendation algorithm and framework in python, 2019, https://github.com/jfkirk/tensorrec [Online; accessed 04.05.2019]. J. Constine, How facebook news feed works techcrunch, 2016, https://techcrunch.com/2016/09/06/ultimate-guide-to-the-news-feed/ Twitter trends Faqs, https://help.twitter.com/en/using-twitter/twitter-trending-faqs. K. Liu, P. Natarajan, WMRB: Learning to Rank in a Scalable Batch Training Approach, http://arxiv.org/abs/1711.04015 [Cs, Stat] (2017). (accessed December 19, 2019). Sun, 2004, Explanation of log-normal distributions and power-law distributions in biology and social science Kuninaka, 2012, Statistical properties of complex systems-lognormal and related distributions, 1468, 241, 10.1063/1.4745586 A. Thieltges, O. Papakyriakopoulos, J.C.M. Serrano, S. Hegelich, Effects of Social Bots in the Iran-Debate on Twitter, http://arxiv.org/abs/1805.10105 [Cs] (2018). (accessed December 19, 2019). Shao, 2018, The spread of low-credibility content by social bots, Nat. Commun., 9, 4787, 10.1038/s41467-018-06930-7 Correa, 2010, Who interacts on the web?: the intersection of users personality and social media use, Comput. Hum. Behav., 26, 247, 10.1016/j.chb.2009.09.003 Duggan, 2013, The demographics of social media users - 2012 Lin, 2011, More voices than ever? Quantifying media bias in networks, 1 Zhou, 2006, Training cost-sensitive neural networks with methods addressing the class imbalance problem, IEEE Trans. Knowl. Data Eng., 18, 63, 10.1109/TKDE.2006.17 Pan, 2008, One-class collaborative filtering, 502 Lee, 1999, Robust radial basis function neural networks, IEEE Trans. Syst. Man Cybern. Part B (Cybernetics), 29, 674, 10.1109/3477.809023 A. Kurakin, I. Goodfellow, S. Bengio, Adversarial examples in the physical world, http://arxiv.org/abs/1607.02533 [Cs, Stat] (2017). (accessed December 19, 2019). K. Grosse, P. Manoharan, N. Papernot, M. Backes, P. McDaniel, On the (Statistical) Detection of Adversarial Examples, http://arxiv.org/abs/1702.06280 [Cs, Stat] (2017). (accessed December 19, 2019). Engelmann, 2018, A democracy called Facebook? Participation as a privacy strategy on social media, 91 Falch, 2009, Business models in social networking, 1 Mittelstadt, 2016, The ethics of algorithms: Mapping the debate, Big Data Soc., 3, 10.1177/2053951716679679 Nissenbaum, 2011, From preemption to circumvention: if technology regulates, why do we need regulation (and vice versa), Berk. Technol. Law J., 26, 1367 Burrell, 2016, How the machine thinks: understanding opacity in machine learning algorithms, Big Data Soc., 3, 10.1177/2053951715622512 Sandvig, 2014, Auditing algorithms: research methods for detecting discrimination on internet platforms, 1 Diakopoulos, 2014, Algorithmic-accountability: the investigation of Black Boxes Pasquale, 2015