Meinungsdynamik und -manipulation durch Social Bots

Adrian Schneider M.A.

doi:10.5771/9783828878587

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Bibliographic data

Copyright year: 2022
ISBN-Print: 978-3-8288-4754-5
ISBN-Online: 978-3-8288-7858-7
Publisher: Tectum, Baden-Baden
Series: Politik begreifen
Volume: 28
Language: German
Pages: 150
Product type: Book Titles

Titelei/Inhaltsverzeichnis No access Pages I - XVIII
1. 1.1 Die Digitalisierung der Öffentlichkeit No access
2. 1.2 Zielsetzung, Forschungsfragen und Überblick No access
2 Methodologische Vorüberlegungen zur agentenbasierten Modellierung No access Pages 9 - 14
1. 3.1 Strukturelle Eigenschaften komplexer sozialer Netzwerke No access
2. 1. 3.2.1 Theoretische Verortung des Backfire-Effekts No access
  2. 3.2.2 Sozialer Einfluss und Konformität No access
  3. 3.2.3 Formale Modelle von Meinungsdynamiken und sozialem Einfluss No access
3. 3.3 Interaktion von Menschen und Social Bots in sozialen Online-Netzwerken No access
4. 3.4 Meinungsmanipulation durch Social Bots No access
5. 3.5 Zusammenfassung und Beschreibung des konzeptuellen Modells No access
1. 4.1 Echokammerhypothese und Meinungsdiversität in sozialen Online-Netzwerken No access
2. 4.2 Simulation von Meinungsdynamiken No access
1. 5.1 Modellwelt No access
2. 5.2 Verhalten menschlicher Agenten No access
3. 5.3 Verhalten von Social Bots No access
1. 1. ticks No access
  2. network-size No access
  3. m No access
  4. Opinion-distribution No access
  5. number-of-bots No access
  6. bot-opinion No access
  7. bot-strategy No access
  8. epsilon No access
  9. backfire-strength No access
2. 1. 6.2.1 Verifizierung der Modell-Implementierung No access
  2. 6.2.2 Einfluss des Toleranzparameters auf die Meinungsdynamik im Basis-Modell No access
  3. 6.2.3 Einfluss von Netzwerkdichte und Zentralität der Agenten auf die Meinungsdynamik im Basis-Modell No access
1. 7.1 Meinungsdynamik im Backfire-Modell No access
2. 1. 7.2.1 Einfluss von Social Bots im Modell mit bounded confidence No access
  2. 7.2.2 Einfluss von Social Bots im Modell mit Backfire-Effekt No access
1. 8.1 Einfluss des Backfire-Effekts auf Meinungsdynamiken in sozialen Online-Netzwerken No access
2. 8.2 Bedingungen einer erfolgreichen Manipulation der öffentlichen Meinung in sozialen Online-Netzwerken No access
9 Limitationen No access Pages 107 - 110
10 Zusammenfassung und Ausblick No access Pages 111 - 114
11 Literaturverzeichnis No access Pages 115 - 122
1. ODD-Protokoll No access
2. Purpose and Patterns No access
3. Entities, State Variables, and Scales No access
4. Process Overview and Scheduling No access
5. 1. Basic principles. No access
  2. Emergence. No access
  3. Adaptation. No access
  4. Sensing. No access
  5. Interaction. No access
  6. Stochasticity. No access
  7. Collectives. No access
  8. Observation. No access
6. 1. setup No access
  2. generate-output No access
  3. setup-humans No access
  4. initialize-human-variables No access
  5. setup-bots No access
  6. initialize-bot-variables No access
  7. layout No access
7. Input Data No access
8. 1. do-motivated-reasoning No access
  2. update-output No access
  3. test-motivated-reasoning No access
  4. recolor No access
1. Effekt von epsilon auf die Dynamik des Basis-Modells No access
2. Effekt der Netzwerkdichte auf die Dauer der Simulation im Basis-Modell No access
3. Effekt von Epsilon auf die Varianz der finalen Meinungsverteilung für unterschiedliche Werte von m im Basis-Modell No access
4. Koeffizientenplots der linearen Regressionsmodelle zur Analyse des Einflusses von Social Bots No access

Bibliography (105 entries)

Abokhodair, N., Yoo, D. & McDonald, D.W., 2015, ‘Dissecting a social botnet: Growth, content and influence in Twitter’, Proceedings of the 18th ACM conference on computer supported cooperative work & social computing, pp. 839–851. Open Google Scholar doi.org/10.5771/9783828878587
Asch, S.E., 1955, ‘Opinions and Social Pressure’, Scientific American 193(5), 31–35, from http://www.jstor.org/stable/24943779. Open Google Scholar doi.org/10.5771/9783828878587
Asch, S.E., 1956, ‘Studies of independence and conformity: I. A minority of one against a unanimous majority’, Psychological Monographs: General and Applied, 70(9), 1–70, Psychological Monographs: General and Applied 70(9), 1–70. Open Google Scholar doi.org/10.5771/9783828878587
Bail, C.A., Argyle, L.P., Brown, T.W., Bumpus, J.P., Chen, H. & Hunzaker, M.B.F., et al., 2018, ‘Exposure to opposing views on social media can increase political polarization’, Proceedings of the National Academy of Sciences of the United States of America 115(37), 9216–9221. Open Google Scholar doi.org/10.5771/9783828878587
Bakshy, E., Messing, S. & Adamic, L.A., 2015, ‘Exposure to ideologically diverse news and opinion on Facebook’, Science 348(6239), 1130–1132, from https://science.sciencemag.org/content/348/6239/1130. Open Google Scholar doi.org/10.5771/9783828878587
Barabasi & Albert, 1999, ‘Emergence of scaling in random networks’, Science (New York, N.Y.) 286(5439), 509–512. Open Google Scholar doi.org/10.5771/9783828878587
Barberá, P., 2014, ‘How social media reduces mass political polarization. Evidence from Germany, Spain, and the US’, Job Market Paper, New York University 46. Open Google Scholar doi.org/10.5771/9783828878587
Bessi, A. & Ferrara, E., 2016, ‘Social Bots Distort the 2016 US Presidential Election Online Discussion’, First Monday 21(11). Open Google Scholar doi.org/10.5771/9783828878587
Bond, R.M., Fariss, C.J., Jones, J.J., Kramer, A.D.I., Marlow, C. & Settle, J.E., et al., 2012, ‘A 61-million-person experiment in social influence and political mobilization’, Nature 489(7415), 295–298, from https://www.nature.com/articles/nature11421. Open Google Scholar doi.org/10.5771/9783828878587
Brachten, F., Stieglitz, S., Hofeditz, L., Kloppenborg, K. & Reimann, A., 2017, Strategies and Influence of Social Bots in a 2017 German state election – A case study on Twitter, from https://arxiv.org/pdf/1710.07562. Open Google Scholar doi.org/10.5771/9783828878587
Cialdini, R.B. & Goldstein, N.J., 2004, ‘Social influence: compliance and conformity’, Annual review of psychology 55, 591–621. Open Google Scholar doi.org/10.5771/9783828878587
Cinelli, M., Francisci Morales, G. de, Galeazzi, A., Quattrociocchi, W. & Starnini, M., 2021, ‘The echo chamber effect on social media’, Proceedings of the National Academy of Sciences of the United States of America 118(9). Open Google Scholar doi.org/10.5771/9783828878587
Coleman, J.S., 1986, ‘Social Theory, Social Research, and a Theory of Action’, American Journal of Sociology 91(6), 1309–1335. Open Google Scholar doi.org/10.5771/9783828878587
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F. & Flammini, A., 2011, ‘Political Polarization on Twitter’, Proceedings of the International AAAI Conference on Web and Social Media 5(1), from https://ojs.aaai.org/index.php/ICWSM/article/view/14126. Open Google Scholar doi.org/10.5771/9783828878587
Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G. & Kertesz, J., et al., 2012, ‘Manifesto of computational social science’, The European Physical Journal Special Topics 214(1), 325–346. Open Google Scholar doi.org/10.5771/9783828878587
Cusimano, C. & Lombrozo, T., 2021, ‘Morality justifies motivated reasoning in the folk ethics of belief’, Cognition 2021, 104513, from https://www.sciencedirect.com/science/article/pii/S0010027720303322. Open Google Scholar doi.org/10.5771/9783828878587
Das, A., Gollapudi, S. & Munagala, K., 2014, ‘Modeling opinion dynamics in social networks’, in B. Carterette (ed.), Proceedings of the 7th ACM International Conference on Web Search and Data Mining: February 24 – 28, 2014, New York, NY, USA, New York, New York, USA, 2/24/2014 – 2/28/2014, pp. 403–412. Open Google Scholar doi.org/10.5771/9783828878587
David, B. & Turner, J.C., 1999, ‘Studies in self-categorization and minority conversion: The in-group minority in intragroup and intergroup contexts’, British Journal of Social Psychology 38(2), 115–134, from https://bpspsychub.onlinelibrary.wiley.com/doi/10.1348/014466699164086. Open Google Scholar doi.org/10.5771/9783828878587
Deffuant, G., Amblard, F., Weisbuch, G. & Faure, T., 2002, ‘How Can Extremism Prevail? A Study Based on the Relative Agreement Interaction Model’, Journal of artificial societies and social simulation 5(4), from http://jasss.soc.surrey.ac.uk/5/4/1.html. Open Google Scholar doi.org/10.5771/9783828878587
Deffuant, G., Neau, D., Amblard, F. & Weisbuch, G., 2000, ‘Mixing beliefs among interacting agents’, Advances in Complex Systems 03(01n04), 87–98. Open Google Scholar doi.org/10.5771/9783828878587
Degroot, M.H., 1974, ‘Reaching a Consensus’, Journal of the American Statistical Association 69(345), 118–121. Open Google Scholar doi.org/10.5771/9783828878587
Deutsch, M. & Gerard, H.B., 1955, ‘A study of normative and informational social influences upon individual judgement’, Journal of abnormal psychology 51(3), 629–636. Open Google Scholar doi.org/10.5771/9783828878587
Dodds, P.S., Muhamad, R. & Watts, D.J., 2003, ‘An experimental study of search in global social networks’, Science 301(5634), 827–829. Open Google Scholar doi.org/10.5771/9783828878587
Dommers, S., van der Hofstad, R. & Hooghiemstra, G., 2010, ‘Diameters in Preferential Attachment Models’, Journal of Statistical Physics 139(1), 72–107. Open Google Scholar doi.org/10.5771/9783828878587
Dubois, E. & Blank, G., 2018, ‘The echo chamber is overstated: the moderating effect of political interest and diverse media’, Information, Communication & Society 21(5), 729–745. Open Google Scholar doi.org/10.5771/9783828878587
Edwards, C., Edwards, A., Spence, P.R. & Shelton, A.K., 2014, ‘Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter’, Computers in Human Behavior 33, 372–376, from http://www.sciencedirect.com/science/article/pii/S0747563213003129. Open Google Scholar doi.org/10.5771/9783828878587
Effing, R., van Hillegersberg, J. & Huibers, T., 2011, ‘Social Media and Political Participation: Are Facebook, Twitter and YouTube Democratizing Our Political Systems?’, in E. Tambouris (ed.), Electronic participation: Third IFIP WG 8.5 international conference, EPart 2011, Delft, The Netherlands, August 29 – September 1, 2011; proceedings, Berlin, Heidelberg, 2011, pp. 25–35. Open Google Scholar doi.org/10.5771/9783828878587
EU vs DISINFORMATION, 2021, Vilifying Germany; Wooing Germany – EU vs DISINFORMATION, viewed 10 March 2021, from https://euvsdisinfo.eu/villifying-germany-wooing-germany/. Open Google Scholar doi.org/10.5771/9783828878587
Everett, R.M., Nurse, J.R.C. & Erola, A., 2016, ‘The anatomy of online deception’, in S. Ossowski (ed.), Proceedings of the 31st Annual ACM Symposium on Applied Computing – SAC '16, Pisa, Italy, 04.04.2016 – 08.04.2016, pp. 1115–1120. Open Google Scholar doi.org/10.5771/9783828878587
Ferrara, E., Varol, O., Davis, C., Menczer, F. & Flammini, A., 2016, ‘The rise of social bots’, Communications of the ACM 59(7), 96–104. Open Google Scholar doi.org/10.5771/9783828878587
Festinger, L., 1962, A theory of cognitive dissonance, Stanford university press. Open Google Scholar doi.org/10.5771/9783828878587
French, J. R., 1956, ‘A formal theory of social power’, Psychological review 63(3), 181–194. Open Google Scholar doi.org/10.5771/9783828878587
Friedkin, N.E. & Johnsen, E.C., 1990, ‘Social influence and opinions’, The Journal of Mathematical Sociology 15(3–4), 193–206. Open Google Scholar doi.org/10.5771/9783828878587
Friedkin, N.E. & Johnsen, E.С., 1999, ‘Social influence networks and opinion change’, Advances in Group Processes 16, 1–29. Open Google Scholar doi.org/10.5771/9783828878587
Fu, F., Liu, L. & Wang, L., 2008, ‘Empirical analysis of online social networks in the age of Web 2.0’, Physica A: Statistical Mechanics and its Applications 387(2–3), 675–684, from http://www.sciencedirect.com/science/article/pii/S0378437107010631. Open Google Scholar doi.org/10.5771/9783828878587
Gilbert, N., 2007, Agent-based models, Sage publications, Thousand Oaks. Open Google Scholar doi.org/10.5771/9783828878587
Gómez-Serrano, J., Graham, C. & Le Boudec, J.-Y., 2010, The Bounded Confidence Model Of Opinion Dynamics, Cornell University, from http://arxiv.org/pdf/1006.3798v3. Open Google Scholar doi.org/10.5771/9783828878587
Graebner, C., 2018, ‘How to Relate Models to Reality? An Epistemological Framework for the Validation and Verification of Computational Models’, Journal of Artificial Societies and Social Simulation 21(3). Open Google Scholar doi.org/10.5771/9783828878587
Granovetter, M.S., 1973, ‘The strength of weak ties’, American Journal of Sociology 78(6), 1360–1380. Open Google Scholar doi.org/10.5771/9783828878587
Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V. & Giske, J., et al., 2006, ‘A standard protocol for describing individual-based and agent-based models’, Ecological Modelling 198(1–2), 115–126. Open Google Scholar doi.org/10.5771/9783828878587
Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J. & Railsback, S.F., 2010, ‘The ODD protocol: A review and first update’, Ecological Modelling 221(23), 2760–2768. Open Google Scholar doi.org/10.5771/9783828878587
Habermas, J., 1982, Strukturwandel der Öffentlichkeit: Untersuchungen zu einer Kategorie der bürgerlichen Gesellschaft, Luchterhand. Open Google Scholar doi.org/10.5771/9783828878587
Hagen, L.M., Au, A.-M. in der & Wieland, M., 2017, ‘Polarisierung im Social Web und der intervenierende Effekt von Bildung: eine Untersuchung zu den Folgen algorithmischer Medien am Beispiel der Zustimmung zu Merkels "Wir schaffen das!’, kommunikation @ gesellschaft 18, 1–20. Open Google Scholar doi.org/10.5771/9783828878587
Haglin, K., 2017, ‘The limitations of the backfire effect’, Research & Politics 4(3), 205316801771654. Open Google Scholar doi.org/10.5771/9783828878587
Hansen, P.G., Hendricks, V.F. & Rendsvig, R.K., 2013, ‘Infostorms’, Metaphilosophy 44(3), 301–326. Open Google Scholar doi.org/10.5771/9783828878587
Harary, F., 1959, ‘A criterion for unanimity in French's theory of social power’, Studies in social power, pp. 168–182, Univer. Michigan, Oxford, England. Open Google Scholar doi.org/10.5771/9783828878587
Hegselmann, R. & Krause, U., 2002, ‘Opinion dynamics and bounded confidence.: models, analysis, and simulation’, Journal of artificial societies and social simulation 5(3). Open Google Scholar doi.org/10.5771/9783828878587
Howard, P.N. & Kollanyi, B., 2016, ‘Bots, #Strongerin, and #Brexit: Computational Propaganda During the UK-EU Referendum’, SSRN Electronic Journal 2016. Open Google Scholar doi.org/10.5771/9783828878587
Jansen, D., 1999, Einführung in die Netzwerkanalyse, VS Verlag für Sozialwissenschaften. Open Google Scholar doi.org/10.5771/9783828878587
Jost, J.T., Barberá, P., Bonneau, R., Langer, M., Metzger, M. & Nagler, J., et al., 2018, ‘How Social Media Facilitates Political Protest: Information, Motivation, and Social Networks’, Political Psychology 39, 85–118. Open Google Scholar doi.org/10.5771/9783828878587
Kaplan, A.M. & Haenlein, M., 2010, ‘Users of the world, unite! The challenges and opportunities of Social Media’, Business Horizons 53(1), 59–68, from http://www.sciencedirect.com/science/article/pii/S0007681309001232. Open Google Scholar doi.org/10.5771/9783828878587
Keuschnigg, M., 2012, ‘Konformität durch Herdenverhalten’, KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie 64(1), 1–36. Open Google Scholar doi.org/10.5771/9783828878587
Klein, D., Marx, J. & Fischbach, K., 2018, ‘Agent-Based Modeling in Social Science, History, and Philosophy. An Introduction’, Historical Social Research / Historische Sozialforschung 43(1 (163)), 7–27, from http://www.jstor.org/stable/26395042. Open Google Scholar doi.org/10.5771/9783828878587
Knobloch-Westerwick, S., 2007, ‘Kognitive Dissonanz »Revisited«’, Publizistik 52(1), 51–62. Open Google Scholar doi.org/10.5771/9783828878587
Kollanyi, B., Howard, P.N. & Woolley, S.C., 2016, ‘Bots and automation over Twitter during the first US presidential debate’, Comprop data memo 1, 1–4. Open Google Scholar doi.org/10.5771/9783828878587
Kunda, Z., 1990, ‘The case for motivated reasoning’, Psychological bulletin 108(3), 480–498. Open Google Scholar doi.org/10.5771/9783828878587
Lee, J.K., Choi, J., Kim, C. & Kim, Y., 2014, ‘Social Media, Network Heterogeneity, and Opinion Polarization’, Journal of Communication 64(4), 702–722. Open Google Scholar doi.org/10.5771/9783828878587
Lischka, K. & Stöcker, C., 2017, Digitale Öffentlichkeit. Open Google Scholar doi.org/10.5771/9783828878587
Loader, B.D., Vromen, A. & Xenos, M.A., 2014, ‘The networked young citizen: social media, political participation and civic engagement’, Information, Communication & Society 17(2), 143–150. Open Google Scholar doi.org/10.5771/9783828878587
Lorenz, J., 2007, ‘Continous Opinion Dynamics Under Bounded Confidence: A Survey’, International Journal of Modern Physics C 18(12), 1819–1838. Open Google Scholar doi.org/10.5771/9783828878587
Macy, M.W. & Willer, R., 2002, ‘From Factors to Actors: Computational Sociology and Agent-Based Modeling’, Annual Review of Sociology 28(1), 143–166. Open Google Scholar doi.org/10.5771/9783828878587
Mallinson, D.J. & Hatemi, P.K., 2018, ‘The effects of information and social conformity on opinion change’, PLOS ONE 13(5), e0196600, from https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196600. Open Google Scholar doi.org/10.5771/9783828878587
Matakos, A., Terzi, E. & Tsaparas, P., 2017, ‘Measuring and moderating opinion polarization in social networks’, Data Mining and Knowledge Discovery 31(5), 1480–1505, from https://link.springer.com/article/10.1007/s10618-017-0527-9. Open Google Scholar doi.org/10.5771/9783828878587
McPherson, M., Smith-Lovin, L. & Cook, J.M., 2001, ‘Birds of a Feather: Homophily in Social Networks’, Annual Review of Sociology 27(1), 415–444. Open Google Scholar doi.org/10.5771/9783828878587
Milgram, S., 1967, ‘The small world problem’, Psychology today 2(1), 60–67. Open Google Scholar doi.org/10.5771/9783828878587
Miller, B., 2016, ‘Three and a half degrees of separation’, Facebook Research, 4 February, viewed 22 March 2021, from https://research.fb.com/three-and-a-half-degrees-of-separation/. Open Google Scholar doi.org/10.5771/9783828878587
Moscovici, S. & Faucheux, C., 2000, ‘Social Influence, Conformity Bias, and the Study of Active Minorities’, in L. Berkowitz (ed.), Advances in experimental social psychology, vol. 6, pp. 149–202, Elsevier, Amsterdam, Heidelberg, from https://www.sciencedirect.com/science/article/pii/S0065260108600271. Open Google Scholar doi.org/10.5771/9783828878587
Murthy, D., Powell, A.B., Tinati, R., Anstead, N., Carr, L. & Halford, S.J., et al., 2016, ‘Automation, Algorithms, and Politics| Bots and Political Influence: A Sociotechnical Investigation of Social Network Capital’, International Journal of Communication 10(0), 20. Open Google Scholar doi.org/10.5771/9783828878587
Myers, D.G. & Lamm, H., 1975, ‘The Polarizing Effect of Group Discussion: The discovery that discussion tends to enhance the average prediscussion tendency has stimulated new insights about the nature of group influence’, American Scientist 63(3), 297–303, from www.jstor.org/stable/27845465. Open Google Scholar doi.org/10.5771/9783828878587
Neubaum, G., Rösner, L., Ganster, T., Hambach, K. & Krämer, N.C., 2018, ‘United in the name of justice: How conformity processes in social media may influence online vigilantism’, Psychology of Popular Media Culture 7(2), 185–199. Open Google Scholar doi.org/10.5771/9783828878587
Newman, N., Fletcher, R., Schulz, A., Simge, A. & Nielsen, R.K., 2020, Digital News Report 2020. Open Google Scholar doi.org/10.5771/9783828878587
Nickerson, R.S., 1998, ‘Confirmation Bias: A Ubiquitous Phenomenon in Many Guises’, Review of General Psychology 2(2), 175–220. Open Google Scholar doi.org/10.5771/9783828878587
Noelle-Neumann, E., 1974, ‘The Spiral of Silence a Theory of Public Opinion’, Journal of Communication 24(2), 43–51. Open Google Scholar doi.org/10.5771/9783828878587
Nyhan, B. & Reifler, J., 2010, ‘When Corrections Fail: The Persistence of Political Misperceptions’, Political Behavior 32(2), 303–330. Open Google Scholar doi.org/10.5771/9783828878587
Nyhan, B. & Reifler, J., 2015, ‘Does correcting myths about the flu vaccine work? An experimental evaluation of the effects of corrective information’, Vaccine 33(3), 459–464. Open Google Scholar doi.org/10.5771/9783828878587
O’Connor, C. & Weatherall, J.O., 2018, ‘Scientific polarization’, European Journal for Philosophy of Science 8(3), 855–875. Open Google Scholar doi.org/10.5771/9783828878587
O'Connor, C. & Weatherall, J.O., 2019, The misinformation age: How false beliefs spread, Yale university press, New Haven, Conn., London. Open Google Scholar doi.org/10.5771/9783828878587
Pariser, E., 2011, The filter bubble: What the internet is hiding from you, The Penguin Press, New York. Open Google Scholar doi.org/10.5771/9783828878587
Postmes, T., Spears, R., Sakhel, K. & Groot, D. de, 2001, ‘Social Influence in Computer-Mediated Communication: The Effects of Anonymity on Group Behavior’, Personality and Social Psychology Bulletin 27(10), 1243–1254. Open Google Scholar doi.org/10.5771/9783828878587
Railsback, S.F. & Grimm, V., 2019, Agent-based and individual-based modeling: A practical introduction, 2nd edn., Princeton University Press, Princeton, NJ. Open Google Scholar doi.org/10.5771/9783828878587
Ratkiewicz, J., Conover, M.D., Meiss, M., Gonçalves, B., Flammini, A. & Menczer, F.M., 2011, ‘Detecting and tracking political abuse in social media’, Fifth international AAAI conference on weblogs and social media, pp. 297–304. Open Google Scholar doi.org/10.5771/9783828878587
Rau, J.P. & Stier, S., 2019, ‘Die Echokammer-Hypothese: Fragmentierung der Öffentlichkeit und politische Polarisierung durch digitale Medien?’, Zeitschrift für Vergleichende Politikwissenschaft 13(3), 399–417. Open Google Scholar doi.org/10.5771/9783828878587
Reeves, B. & Nass, C.I., 1996, The media equation: How people treat computers, television, and new media like real people and places, Cambridge university press. Open Google Scholar doi.org/10.5771/9783828878587
Rosenstock, S., Bruner, J. & O’Connor, C., 2017, ‘In Epistemic Networks, Is Less Really More?’, Philosophy of Science 84(2), 234–252. Open Google Scholar doi.org/10.5771/9783828878587
Ross, B., Pilz, L., Cabrera, B., Brachten, F., Neubaum, G. & Stieglitz, S., 2019, ‘Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks’, European Journal of Information Systems 28(4), 394–412. Open Google Scholar doi.org/10.5771/9783828878587
Scheller, S., 2019, ‘The Strategic Use of Fear Appeals in Political Communication’, Political Communication 36(4), 586–608. Open Google Scholar doi.org/10.5771/9783828878587
Schweiger, W., Weber, P., Prochazka, F. & Brückner, L., 2019, Algorithmisch personalisierte Nachrichtenkanäle: Begriffe, Nutzung, WIrkung, Springer VS, Wiesbaden. Open Google Scholar doi.org/10.5771/9783828878587
Sherif, M., 1936, ‘The psychology of social norms’ 1936. Open Google Scholar doi.org/10.5771/9783828878587
Sola Pool, I. de & Kochen, M., 1978, ‘Contacts and influence’, Social Networks 1(1), 5–51, from https://www.sciencedirect.com/science/article/pii/0378873378900114. Open Google Scholar doi.org/10.5771/9783828878587
Stella, M., Ferrara, E. & Domenico, M. de, 2018, ‘Bots increase exposure to negative and inflammatory content in online social systems’, Proceedings of the National Academy of Sciences of the United States of America 115(49), 12435–12440. Open Google Scholar doi.org/10.5771/9783828878587
Suárez-Serrato, P., Roberts, M.E., Davis, C. & Menczer, F., 2016, ‘On the Influence of Social Bots in Online Protests’, in E. Spiro (ed.), Social informatics: 8th International Conference, SocInfo 2016, Bellevue, WA, USA, November 11–14, 2016 proceedings, part II, Cham, 2016, pp. 269–278. Open Google Scholar doi.org/10.5771/9783828878587
Sunstein, C.R., 2001, Echo chambers: Bush v. Gore, impeachment, and beyond, Princeton University Press, Princeton, N.J. Open Google Scholar doi.org/10.5771/9783828878587
Taber, C.S. & Lodge, M., 2006, ‘Motivated Skepticism in the Evaluation of Political Beliefs’, American Journal of Political Science 50(3), 755–769. Open Google Scholar doi.org/10.5771/9783828878587
Travers, J. & Milgram, S., 1969, ‘An Experimental Study of the Small World Problem’, Sociometry 32(4), 425. Open Google Scholar doi.org/10.5771/9783828878587
Ugander, J., Karrer, B., Backstrom, L. & Marlow, C., 2011, The Anatomy of the Facebook Social Graph, from https://arxiv.org/pdf/1111.4503. Open Google Scholar doi.org/10.5771/9783828878587
Wason, P.C., 1968, ‘Reasoning about a rule’, Quarterly Journal of Experimental Psychology 20(3), 273–281. Open Google Scholar doi.org/10.5771/9783828878587
Watts, D.J. & Strogatz, S.H., 1998, ‘Collective dynamics of 'small-world' networks’, Nature 393(6684), 440–442. Open Google Scholar doi.org/10.5771/9783828878587
Weisbuch, G., 2004, ‘Bounded confidence and social networks’, The European Physical Journal B – Condensed Matter 38(2), 339–343. Open Google Scholar doi.org/10.5771/9783828878587
Weisbuch, G., Deffuant, G., Amblard, F. & Nadal, J.-P., 2003, ‘Interacting Agents and Continuous Opinions Dynamics’, in N. Jonard and R. Cowan (eds.), Heterogenous agents, interactions and economic performance, vol. 521, pp. 225–242, Springer, Berlin. Open Google Scholar doi.org/10.5771/9783828878587
Wells, C., Reedy, J., Gastil, J. & Lee, C., 2009, ‘Information Distortion and Voting Choices: The Origins and Effects of Factual Beliefs in Initiative Elections’, Political Psychology 30(6), 953–969. Open Google Scholar doi.org/10.5771/9783828878587
Wilensky, U., 1999, NetLogo, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL., from http://ccl.northwestern.edu/netlogo/. Open Google Scholar doi.org/10.5771/9783828878587
Wood, T. & Porter, E., 2019, ‘The Elusive Backfire Effect: Mass Attitudes’ Steadfast Factual Adherence’, Political Behavior 41(1), 135–163. Open Google Scholar doi.org/10.5771/9783828878587
Woolley, S.C., 2016, ‘Automating power: Social bot interference in global politics’, First Monday 2016. Open Google Scholar doi.org/10.5771/9783828878587
Xia, H., Wang, H. & Xuan, Z., 2011, ‘Opinion Dynamics: A Multidisciplinary Review and Perspective on Future Research’, International Journal of Knowledge and Systems Science 2(4), 72–91. Open Google Scholar doi.org/10.5771/9783828878587
Zollman, K.J., 2012, ‘Social network structure and the achievement of consensus’, Politics, Philosophy & Economics 11(1), 26–44. Open Google Scholar doi.org/10.5771/9783828878587