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Data Science: Case "Political Communication 1/2"

Department of Communication Science, University of Amsterdam, profile picture
Department of Communication Science, University of Amsterdam

This document introduces the topic of studying social media in political communication. It discusses how social media may contribute to selective exposure, fragmentation, and polarization due to people selectively exposing themselves to ideologically congruent content. It also examines how politicians are using social media to directly communicate with citizens. The case study aims to leverage computational social science methods and large-scale data analysis to provide new insights into these topics. Students will work on a project analyzing political content on social media over the course of the case study.

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Related topics:
Data Science Insights • Computational Social Science
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Introducing. . . Studying social media in poltical communication Conclusion Fundamentals of Data Science: Case “Political Communication” Damian Trilling d.c.trilling@uva.nl @damian0604 www.damiantrilling.net Afdeling Communicatiewetenschap Universiteit van Amsterdam 12-09-2016 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Today 1 Introducing. . . . . . the people . . . the schedule . . . the topic 2 Studying social media in poltical communication Selective exposure and filter bubbles Fragmentation Polarization Politicians on social media Social media and public opinion 3 Conclusion Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the people Introducing. . . . . . the people Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the people Introducing. . . Stevan dr. Stevan Rudinac Postdoctoral Researcher @ Intelligent Sensory Information Systems // IvI // UvA • received PhD degree in Computer Science @ TU Delft 2013 • graduated in Electrical Engineering @ University of Belgrade 2006 • worked @ NFI, TU Delft, TU Eindhoven and University of Belgrade • interested in multimedia information retrieval with a focus on urban computing and security applications. s.rudinac@uva.nl Science Park 904 C3.253 https://staff.fnwi.uva.nl/s.rudinac/ Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the people Introducing. . . Damian dr. Damian Trilling Assistant Professor Political Communication & Journalism • studied Communication Science in Münster and at the VU 2003–2009 • PhD candidate @ UvA 2009–2012 • interested in political communication and journalism in a changing media environment and in innovative (digital, large-scale, computational) research methods @damian0604 d.c.trilling@uva.nl REC-C 8th floor www.damiantrilling.net Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the people Introducing. . . You Your name? Your background? Your reason to follow this course? Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the schedule Introducing. . . . . . the schedule Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Monday, 12 Sept., 11–13 (Damian) Intro to our social science case: Social media analysis in political communication Tuesday, 13 Sept., 9-11 (Stevan) The research pipeline Working with the Twitter API Data preprocessing and sentiment analysis Tuesday, 13 Sept., 13-17 (you) Project: It’s your turn! Thursday, 15 Sept., 9-11 (Stevan & Damian) Practical work: Helping you with the project Thursday, 15 Sept., 11-17 (you) Project: It’s your turn! Thursday, 15 Sept., 17-19 (Stevan & Damian) Presentations: Teams pitching the progress
Monday, 19 Sept., 11–13 (Damian) Analyzing political content on social media: Examples of research so far Tuesday, 20 Sept., 9-11 (Stevan) Topic analysis, correlations, visualization Guest presentation Joost Boonzajer Flaes, Twitter UK Tuesday, 20 Sept., 13-17 (you) Project: It’s your turn! Thursday, 22 Sept., 9-11 (Stevan & Damian) Practical work: Helping you with the project Thursday, 22 Sept., 11-17 (you) Project: It’s your turn! Thursday, 22 Sept., 17-19 (Stevan & Damian) Presentations: Teams pitching the final results
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Introducing. . . . . . the topic Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic This case is about social sciences ⇒communication science ⇒political communication Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic What is Communication Science? • looks at communication between actors in society • is one of the empirical social sciences • (mainly) focuses on mediated communication rather than interpersonal communication Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Political communication journalists citizens political actors Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Methods to study communication qualitative • discourse analysis • interviews • focus groups Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Methods to study communication qualitative • discourse analysis • interviews • focus groups quantitative • survey • experiment • content analysis • network analysis Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Methods to study communication quantitative • survey • experiment • content analysis • network analysis Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic The link with data science • “Computational social science” Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., . . . van Alstyne, M. (2009). Computational social science. Science, 323, 721–723. doi:10.1126/science.1167742 Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12. doi:10.1177/2053951714528481 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic The link with data science • “Computational social science” • “In short, a computational social science is emerging that leverages the capacity to collect and analyze data with an unprecedented breadth and depth and scale.’ (Lazer et al.) Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., . . . van Alstyne, M. (2009). Computational social science. Science, 323, 721–723. doi:10.1126/science.1167742 Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12. doi:10.1177/2053951714528481 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion . . . the topic The link with data science • “Computational social science” • “In short, a computational social science is emerging that leverages the capacity to collect and analyze data with an unprecedented breadth and depth and scale.’ (Lazer et al.) • “the computational social sciences employ the scientific method, complementing descriptive statistics with inferential statistics that seek to identify associations and causality. In other words, they are underpinned by an epistemology wherein the aim is to produce sophisticated statistical models that explain, simulate and predict human life.” (Kitchin) Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., . . . van Alstyne, M. (2009). Computational social science. Science, 323, 721–723. doi:10.1126/science.1167742 Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12. doi:10.1177/2053951714528481 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Studying social media in poltical communication Selective exposure and filter bubbles
Data Science: Case "Political Communication 1/2"
Introducing. . . Studying social media in poltical communication Conclusion Selective exposure and filter bubbles Avoiding dissonant information is human. Festinger, 1956 • People tend to avoid cognitive dissonance • One effective way: avoiding information that conflicts pre-existing beliefs Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Selective exposure and filter bubbles And it does happen in political communication. Lazarsfeld, Berelson, & Gaudet, 1944 • Republicans are mainly exposed to the Republican campaign • Democrats are mainly exposed to the Democratic campaign Fundamentals of Data Science: Case “Political Communication” Damian Trilling
All very well. But where can people actually be selective?
Data Science: Case "Political Communication 1/2"
Introducing. . . Studying social media in poltical communication Conclusion Fragmentation Fragmentation Sunstein, 2001 (and many others) • People will only use those news media that cater to their interest • “echo chambers” • Loss of a common core of issues • Loss of democratic discourse ⇒ news avoidance, entertainment preference as predictor of news use in the new media ecosystem (Prior, 2015) Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Polarization Polarization Selective exposure to ideologically congruent content • If people don’t hear the other side any more, they become more extreme Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Polarization US Presidential Elections: Vote share 1952, 1956 http://xenocrypt.blogspot.de/2013/02/presidential-results-by-1952-districts.html Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Polarization US Presidential Elections: Vote share 2008, 2012 http://xenocrypt.blogspot.de/2013/02/presidential-results-by-1952-districts.html Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Polarization Let’s conclude. . . • People are selective • Nowadays, there is more media content to choose from • content one politically agrees with ⇒ polarization • entertainment over politics, only exposure to topics one is interested in beforehand ⇒ fragmentation Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Polarization The filter bubble Pariser, 2011 • Algorithms increasingly guess what we might like and choose for us (FB, Google,. . . ) • Even if we do not avoid actively, we are living in a filter bubble Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F. (2012). Partisan asymmetries in online political activity. EPJ Data Science, 1(6), 1–19.
Introducing. . . Studying social media in poltical communication Conclusion Polarization How much of an echo chamber are social media? Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Polarization How much of an echo chamber are social media? “We estimated ideological preferences of 3.8 million Twitter users and, using a data set of nearly 150 million tweets concerning 12 political and nonpolitical issues. [...] Overall, we conclude that previous work may have overestimated the degree of ideological segregation in social-media usage” Barbera, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Polarization How much of an echo chamber are social media? “We estimated ideological preferences of 3.8 million Twitter users and, using a data set of nearly 150 million tweets concerning 12 political and nonpolitical issues. [...] Overall, we conclude that previous work may have overestimated the degree of ideological segregation in social-media usage” Barbera, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542. “We find that users share news in similar ways regardless of outlet or perceived ideology of outlet, and that as a user shares more news content, they tend to quickly include outlets with opposing viewpoints. [...] Specifically, users in our sample who sent multiple tweets tended to increase the ideological diversity in news they shared within two or three tweets” Morgan, J. S., Shafiq, M. Z., & Lampe, C. (2013). Is news sharing on Twitter ideologically biased? Proceedings of the 2013 conference on Computer supported cooperative work (pp. 887–897). ACM. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Polarization A first answer to the question why we should study social media: They might change the way people are exposed to news and political messages – which could lead to fragmentation and polarization. But we don’t have conclusive answers yet. . . Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Studying social media in poltical communication Politicians on social media
Introducing. . . Studying social media in poltical communication Conclusion Politicians on social media The politician–citizen edge is now finally a viable way . . . no need to take the detour through mass media any more. journalists citizens political actors Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Politicians on social media Consequences • politicians use social media to be more in control, bypassing the journalistic filter • reach other target groups • but also: from one-way to two-way communication Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Politicians on social media How politicians (should) communicate only • interactivity • personalization can enhance political involvement Kruikemeier, S., van Noort, G., Vliegenthart, R., & de Vreese, C. H. (2013). Getting closer: The effects of personalized and interactive online political communication. European Journal of Communication, 28(1), 53–66. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Politicians on social media Effects of politicians on social media • Using social media impacts voting, especially “voorkeurstemmen” Jacobs, K., & Spierings, N. (2014). . . . Maar win je er stemmen mee ? De impact van Twittergebruik door politici bij de Nederlandse Tweede Kamerverkiezingen. Tijdschrift Voor Communicatiewetenschap, 42(1), 22–38. Kruikemeier, S. (2014). How political candidates use Twitter and the impact on votes.Computers in Human Behavior, 34, 131–139. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Data Science: Case "Political Communication 1/2"
Studying social media in poltical communication Social media and public opnion
Introducing. . . Studying social media in poltical communication Conclusion Social media and public opinion Things to keep in mind • Be careful in generalizing! • Often used because of easy to access API, but is it really the right data source for your question? . . . Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Social media and public opinion Example: Twitter-based prediction of election results Fundamental flaws • heavily skewed user base • better than random does not mean better then more sensible baseline (last election results, . . . ) • published after results were known • arbitrary choices on what to include • overly simplistic assumptions (e.g., number of mentions = support) Gayo-Avello, D. (2013). A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data. Social Science Computer Review, 31(6), 649–679. Jungherr, A., Jürgens, P., & Schoen, H. (2011). Why the Pirate Party Won the German Election of 2009 or The Trouble With Predictions: A Response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. “Predicting Elections With Twitter: What 140 Characters Reveal About Political Sentiment.” Social Science Computer Review, 30(2), 229–234. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Conclusion
Introducing. . . Studying social media in poltical communication Conclusion Conclusion (1) • The changing media environent can have impact on society at large (fragmentation, polarization) • It changes the communication triangle between politicians, journalists, and the public Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Conclusion (2) • Social media data can be linked to political outcomes • But be careful to generalize! Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Introducing. . . Studying social media in poltical communication Conclusion Questions? d.c.trilling@uva.nl @damian0604 www.damiantrilling.net Fundamentals of Data Science: Case “Political Communication” Damian Trilling

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Data Science: Case "Political Communication 1/2"

  • 1. Introducing. . . Studying social media in poltical communication Conclusion Fundamentals of Data Science: Case “Political Communication” Damian Trilling d.c.trilling@uva.nl @damian0604 www.damiantrilling.net Afdeling Communicatiewetenschap Universiteit van Amsterdam 12-09-2016 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 2. Introducing. . . Studying social media in poltical communication Conclusion Today 1 Introducing. . . . . . the people . . . the schedule . . . the topic 2 Studying social media in poltical communication Selective exposure and filter bubbles Fragmentation Polarization Politicians on social media Social media and public opinion 3 Conclusion Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 3. Introducing. . . Studying social media in poltical communication Conclusion . . . the people Introducing. . . . . . the people Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 4. Introducing. . . Studying social media in poltical communication Conclusion . . . the people Introducing. . . Stevan dr. Stevan Rudinac Postdoctoral Researcher @ Intelligent Sensory Information Systems // IvI // UvA • received PhD degree in Computer Science @ TU Delft 2013 • graduated in Electrical Engineering @ University of Belgrade 2006 • worked @ NFI, TU Delft, TU Eindhoven and University of Belgrade • interested in multimedia information retrieval with a focus on urban computing and security applications. s.rudinac@uva.nl Science Park 904 C3.253 https://staff.fnwi.uva.nl/s.rudinac/ Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 5. Introducing. . . Studying social media in poltical communication Conclusion . . . the people Introducing. . . Damian dr. Damian Trilling Assistant Professor Political Communication & Journalism • studied Communication Science in Münster and at the VU 2003–2009 • PhD candidate @ UvA 2009–2012 • interested in political communication and journalism in a changing media environment and in innovative (digital, large-scale, computational) research methods @damian0604 d.c.trilling@uva.nl REC-C 8th floor www.damiantrilling.net Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 6. Introducing. . . Studying social media in poltical communication Conclusion . . . the people Introducing. . . You Your name? Your background? Your reason to follow this course? Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 7. Introducing. . . Studying social media in poltical communication Conclusion . . . the schedule Introducing. . . . . . the schedule Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 8. Monday, 12 Sept., 11–13 (Damian) Intro to our social science case: Social media analysis in political communication Tuesday, 13 Sept., 9-11 (Stevan) The research pipeline Working with the Twitter API Data preprocessing and sentiment analysis Tuesday, 13 Sept., 13-17 (you) Project: It’s your turn! Thursday, 15 Sept., 9-11 (Stevan & Damian) Practical work: Helping you with the project Thursday, 15 Sept., 11-17 (you) Project: It’s your turn! Thursday, 15 Sept., 17-19 (Stevan & Damian) Presentations: Teams pitching the progress
  • 9. Monday, 19 Sept., 11–13 (Damian) Analyzing political content on social media: Examples of research so far Tuesday, 20 Sept., 9-11 (Stevan) Topic analysis, correlations, visualization Guest presentation Joost Boonzajer Flaes, Twitter UK Tuesday, 20 Sept., 13-17 (you) Project: It’s your turn! Thursday, 22 Sept., 9-11 (Stevan & Damian) Practical work: Helping you with the project Thursday, 22 Sept., 11-17 (you) Project: It’s your turn! Thursday, 22 Sept., 17-19 (Stevan & Damian) Presentations: Teams pitching the final results
  • 10. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Introducing. . . . . . the topic Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 11. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic This case is about social sciences ⇒communication science ⇒political communication Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 12. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic What is Communication Science? • looks at communication between actors in society • is one of the empirical social sciences • (mainly) focuses on mediated communication rather than interpersonal communication Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 13. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Political communication journalists citizens political actors Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 14. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Methods to study communication qualitative • discourse analysis • interviews • focus groups Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 15. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Methods to study communication qualitative • discourse analysis • interviews • focus groups quantitative • survey • experiment • content analysis • network analysis Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 16. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic Methods to study communication quantitative • survey • experiment • content analysis • network analysis Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 17. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic The link with data science • “Computational social science” Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., . . . van Alstyne, M. (2009). Computational social science. Science, 323, 721–723. doi:10.1126/science.1167742 Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12. doi:10.1177/2053951714528481 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 18. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic The link with data science • “Computational social science” • “In short, a computational social science is emerging that leverages the capacity to collect and analyze data with an unprecedented breadth and depth and scale.’ (Lazer et al.) Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., . . . van Alstyne, M. (2009). Computational social science. Science, 323, 721–723. doi:10.1126/science.1167742 Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12. doi:10.1177/2053951714528481 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 19. Introducing. . . Studying social media in poltical communication Conclusion . . . the topic The link with data science • “Computational social science” • “In short, a computational social science is emerging that leverages the capacity to collect and analyze data with an unprecedented breadth and depth and scale.’ (Lazer et al.) • “the computational social sciences employ the scientific method, complementing descriptive statistics with inferential statistics that seek to identify associations and causality. In other words, they are underpinned by an epistemology wherein the aim is to produce sophisticated statistical models that explain, simulate and predict human life.” (Kitchin) Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., . . . van Alstyne, M. (2009). Computational social science. Science, 323, 721–723. doi:10.1126/science.1167742 Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12. doi:10.1177/2053951714528481 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 20. Studying social media in poltical communication Selective exposure and filter bubbles
  • 22. Introducing. . . Studying social media in poltical communication Conclusion Selective exposure and filter bubbles Avoiding dissonant information is human. Festinger, 1956 • People tend to avoid cognitive dissonance • One effective way: avoiding information that conflicts pre-existing beliefs Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 23. Introducing. . . Studying social media in poltical communication Conclusion Selective exposure and filter bubbles And it does happen in political communication. Lazarsfeld, Berelson, & Gaudet, 1944 • Republicans are mainly exposed to the Republican campaign • Democrats are mainly exposed to the Democratic campaign Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 24. All very well. But where can people actually be selective?
  • 26. Introducing. . . Studying social media in poltical communication Conclusion Fragmentation Fragmentation Sunstein, 2001 (and many others) • People will only use those news media that cater to their interest • “echo chambers” • Loss of a common core of issues • Loss of democratic discourse ⇒ news avoidance, entertainment preference as predictor of news use in the new media ecosystem (Prior, 2015) Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 27. Introducing. . . Studying social media in poltical communication Conclusion Polarization Polarization Selective exposure to ideologically congruent content • If people don’t hear the other side any more, they become more extreme Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 28. Introducing. . . Studying social media in poltical communication Conclusion Polarization US Presidential Elections: Vote share 1952, 1956 http://xenocrypt.blogspot.de/2013/02/presidential-results-by-1952-districts.html Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 29. Introducing. . . Studying social media in poltical communication Conclusion Polarization US Presidential Elections: Vote share 2008, 2012 http://xenocrypt.blogspot.de/2013/02/presidential-results-by-1952-districts.html Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 30. Introducing. . . Studying social media in poltical communication Conclusion Polarization Let’s conclude. . . • People are selective • Nowadays, there is more media content to choose from • content one politically agrees with ⇒ polarization • entertainment over politics, only exposure to topics one is interested in beforehand ⇒ fragmentation Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 31. Introducing. . . Studying social media in poltical communication Conclusion Polarization The filter bubble Pariser, 2011 • Algorithms increasingly guess what we might like and choose for us (FB, Google,. . . ) • Even if we do not avoid actively, we are living in a filter bubble Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 32. Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F. (2012). Partisan asymmetries in online political activity. EPJ Data Science, 1(6), 1–19.
  • 33. Introducing. . . Studying social media in poltical communication Conclusion Polarization How much of an echo chamber are social media? Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 34. Introducing. . . Studying social media in poltical communication Conclusion Polarization How much of an echo chamber are social media? “We estimated ideological preferences of 3.8 million Twitter users and, using a data set of nearly 150 million tweets concerning 12 political and nonpolitical issues. [...] Overall, we conclude that previous work may have overestimated the degree of ideological segregation in social-media usage” Barbera, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 35. Introducing. . . Studying social media in poltical communication Conclusion Polarization How much of an echo chamber are social media? “We estimated ideological preferences of 3.8 million Twitter users and, using a data set of nearly 150 million tweets concerning 12 political and nonpolitical issues. [...] Overall, we conclude that previous work may have overestimated the degree of ideological segregation in social-media usage” Barbera, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542. “We find that users share news in similar ways regardless of outlet or perceived ideology of outlet, and that as a user shares more news content, they tend to quickly include outlets with opposing viewpoints. [...] Specifically, users in our sample who sent multiple tweets tended to increase the ideological diversity in news they shared within two or three tweets” Morgan, J. S., Shafiq, M. Z., & Lampe, C. (2013). Is news sharing on Twitter ideologically biased? Proceedings of the 2013 conference on Computer supported cooperative work (pp. 887–897). ACM. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 36. Introducing. . . Studying social media in poltical communication Conclusion Polarization A first answer to the question why we should study social media: They might change the way people are exposed to news and political messages – which could lead to fragmentation and polarization. But we don’t have conclusive answers yet. . . Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 37. Studying social media in poltical communication Politicians on social media
  • 38. Introducing. . . Studying social media in poltical communication Conclusion Politicians on social media The politician–citizen edge is now finally a viable way . . . no need to take the detour through mass media any more. journalists citizens political actors Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 39. Introducing. . . Studying social media in poltical communication Conclusion Politicians on social media Consequences • politicians use social media to be more in control, bypassing the journalistic filter • reach other target groups • but also: from one-way to two-way communication Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 40. Introducing. . . Studying social media in poltical communication Conclusion Politicians on social media How politicians (should) communicate only • interactivity • personalization can enhance political involvement Kruikemeier, S., van Noort, G., Vliegenthart, R., & de Vreese, C. H. (2013). Getting closer: The effects of personalized and interactive online political communication. European Journal of Communication, 28(1), 53–66. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 41. Introducing. . . Studying social media in poltical communication Conclusion Politicians on social media Effects of politicians on social media • Using social media impacts voting, especially “voorkeurstemmen” Jacobs, K., & Spierings, N. (2014). . . . Maar win je er stemmen mee ? De impact van Twittergebruik door politici bij de Nederlandse Tweede Kamerverkiezingen. Tijdschrift Voor Communicatiewetenschap, 42(1), 22–38. Kruikemeier, S. (2014). How political candidates use Twitter and the impact on votes.Computers in Human Behavior, 34, 131–139. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 43. Studying social media in poltical communication Social media and public opnion
  • 44. Introducing. . . Studying social media in poltical communication Conclusion Social media and public opinion Things to keep in mind • Be careful in generalizing! • Often used because of easy to access API, but is it really the right data source for your question? . . . Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 45. Introducing. . . Studying social media in poltical communication Conclusion Social media and public opinion Example: Twitter-based prediction of election results Fundamental flaws • heavily skewed user base • better than random does not mean better then more sensible baseline (last election results, . . . ) • published after results were known • arbitrary choices on what to include • overly simplistic assumptions (e.g., number of mentions = support) Gayo-Avello, D. (2013). A Meta-Analysis of State-of-the-Art Electoral Prediction From Twitter Data. Social Science Computer Review, 31(6), 649–679. Jungherr, A., Jürgens, P., & Schoen, H. (2011). Why the Pirate Party Won the German Election of 2009 or The Trouble With Predictions: A Response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. “Predicting Elections With Twitter: What 140 Characters Reveal About Political Sentiment.” Social Science Computer Review, 30(2), 229–234. Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 47. Introducing. . . Studying social media in poltical communication Conclusion Conclusion (1) • The changing media environent can have impact on society at large (fragmentation, polarization) • It changes the communication triangle between politicians, journalists, and the public Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 48. Introducing. . . Studying social media in poltical communication Conclusion Conclusion (2) • Social media data can be linked to political outcomes • But be careful to generalize! Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  • 49. Introducing. . . Studying social media in poltical communication Conclusion Questions? d.c.trilling@uva.nl @damian0604 www.damiantrilling.net Fundamentals of Data Science: Case “Political Communication” Damian Trilling