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Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Fundamentals of...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Last week
1 som...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
This week
Diggi...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Today
1 Analyzi...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Structure
Analy...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Structure
Analy...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Structure
Analy...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Content
Analyzi...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Content
Analyzi...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Content
Systema...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Content
Systema...
Short sidestep:
Agenda setting and Framing
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Agenda setting
...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Framing
“To fra...
Studies that analyze structure of the Twittersphere
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
The Twittersphe...
“In general, famous journalists, experts and politicians are central
actors within the Austrian political Twittersphere an...
“While topics such as the financial crisis were massively
represented in the newspapers and on TV, hardly anyone tweeted
ab...
Studies that analyze the content of the tweets
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Issues
Networks...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Issues
Networks...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Issues
Networks...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Responses to TV...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Responses to TV...
Example 1:
relating word frequencies to each other
Trilling, D. (2015). Two different debates? Investigating the relationsh...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Responses to TV...
Example 2:
manually classify most frequent terms into topics,
subsequent time series analysis
Vergeer, M., & Franses, P. H...
Example 3:
Using external datasource (wikipedia) for topic classification
Yıldırım, A., Üsküdarlı, S., & Özgür, A. (2016). ...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Incivility
Inci...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Incivility
Inci...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Incivility
Inci...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Incivility
Inci...
“Our results suggested that uncivil discourse was highest in
districts that were characterized, in part, by factors tradit...
“A number of limitations temper the present findings. First, the
nature of the data severely limits the generalizability of...
Remember:
This was just a tiny selection to give you some inspiration
about what one can research.
There are a bunch of ot...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Further reading...
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc
Questions?
d.c....
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
Data Science: Case "Political Communication 2/2"
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Data Science: Case "Political Communication 2/2"

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Lecture on Political Communication applications in Data Science. Master Data Science, FNWI UvA

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Data Science: Case "Political Communication 2/2"

  1. 1. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Fundamentals of Data Science: Case “Political Communication” Damian Trilling d.c.trilling@uva.nl @damian0604 www.damiantrilling.net Afdeling Communicatiewetenschap Universiteit van Amsterdam 19-09-2016 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  2. 2. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Last week 1 some themes in political communication research • polarization • fragmentation • and the way politicans use social media 2 Twitter API, preprocessing, geodata, sentiment analysis Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  3. 3. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc This week Digging deeper into the content of the tweets Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  4. 4. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Today 1 Analyzing structure vs analyzing content 2 Short sidestep: Agenda setting and Framing 3 Studies that analyze structure of the Twittersphere 4 Studies that analyze content of tweets Issues Responses to TV debates Incivility 5 Conclusion Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  5. 5. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Structure Analyzing structure vs analyzing content Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  6. 6. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Structure Analyzing Twitter data Analyzing the structure • Number of Tweets over time • singleton/retweet ratio • Distribution of number of Tweets per user • Interaction networks Bruns, A., & Stieglitz, S. (2013). Toward more systematic Twitter analysis: Metrics for tweeting activities. International Journal of Social Research Methodology. doi:10.1080/13645579.2012.756095 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  7. 7. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Structure Analyzing Twitter data Analyzing the structure • Number of Tweets over time • singleton/retweet ratio • Distribution of number of Tweets per user • Interaction networks ⇒ Focus on the amount of content and on the question who interacts with whom, not on what is said Bruns, A., & Stieglitz, S. (2013). Toward more systematic Twitter analysis: Metrics for tweeting activities. International Journal of Social Research Methodology. doi:10.1080/13645579.2012.756095 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  8. 8. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Content Analyzing Twitter data Analyzing the content • Sentiment analysis • Word frequencies • regexp searches • Word cooccurrences (⇒topics, frames) • co-occurrence networks • PCA • LDA • . . . Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  9. 9. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Content Analyzing Twitter data Analyzing the content • Sentiment analysis • Word frequencies • regexp searches • Word cooccurrences (⇒topics, frames) • co-occurrence networks • PCA • LDA • . . . ⇒ Focus on what is said Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  10. 10. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Content Systematizing analytical approaches ⇒ It depends on your reserach question which approach is more interesting! Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  11. 11. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Content Systematizing analytical approaches ⇒ It depends on your reserach question which approach is more interesting! But probably the most interesting thing is to combine them both Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  12. 12. Short sidestep: Agenda setting and Framing
  13. 13. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Agenda setting Beyond simplistic stimulus-response models of media effects: Media effects are not so much about how we think, but what we think aboutMcCombs, M, & Shaw, D (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36: 176. doi:10.1086/267990 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  14. 14. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Framing “To frame is to select some aspects of a perceived reality and make them more salient in a communicating text, in such a way as to promote a particular problem definition, causal interpretation, moral evaluation, and/or treatment recommendation for the item described” Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43, 51–58. doi:10.1111/j.1460-2466.1993.tb01304.x Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  15. 15. Studies that analyze structure of the Twittersphere
  16. 16. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc The Twittersphere Mapping the Austrian Twittersphere • How do politicians, journalists, and citizens interact? • How do topics between news coverage and tweets overlap? (already content) Ausserhofer, J., & Maireder, A. (2013). National Politics on Twitter. Information, Communication & Society, 16(3), 291—314. doi:10.1080/1369118X Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  17. 17. “In general, famous journalists, experts and politicians are central actors within the Austrian political Twittersphere and form their own, dense and influential subnetwork within the broader sphere. Non-professionals may participate in this network, provided that they engage receptive members of the elite who act as ‘bridges’ between subnetworks. However, when the discussion involves certain topics, niche authorities emerge, and these authorities – including a few left-wing activists and bloggers – join other political professionals as central information hubs.” Ausserhofer & Maireder 2013, p. 19
  18. 18. “While topics such as the financial crisis were massively represented in the newspapers and on TV, hardly anyone tweeted about such topics on Twitter. A similar phenomenon could be observed with the ongoing coverage of corruption-related investigations, about which only a few users bothered to tweet. Short-living topics such as the aforementioned ball of the right-wing fraternities and the squatting of an abandoned house and the forced eviction of its ‘residents’ were popular topics on Twitter. A further explanation of why these topics are more popular on Twitter than in mass media is that activists use the service not only to discuss but also to facilitate their activities.” Ausserhofer & Maireder 2013, p. 19
  19. 19. Studies that analyze the content of the tweets
  20. 20. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Issues Networks of issues • Which topics are co-mentioned by the same users? • Which topics are co-mentioned by different types of accounts? Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network Issue Agendas on Twitter During the 2012 U.S. Presidential Election. Journal of Communication, 64, 296–316. doi:10.1111/jcom.12089 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  21. 21. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Issues Networks of issues • Which topics are co-mentioned by the same users? • Which topics are co-mentioned by different types of accounts? • Sentistrength + in combination with Obama/Romney to determine who supports whom • simple keyword searches (dictionary-approach) for topic classification • network analysis Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network Issue Agendas on Twitter During the 2012 U.S. Presidential Election. Journal of Communication, 64, 296–316. doi:10.1111/jcom.12089 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  22. 22. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Issues Networks of issues • General-interest media issue network predicts issue network of Obama supporters • Partisan media issue network predicts issue network of Romney supporters Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network Issue Agendas on Twitter During the 2012 U.S. Presidential Election. Journal of Communication, 64, 296–316. doi:10.1111/jcom.12089 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  23. 23. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Responses to TV debates Second Screen • Linking events to Twitter reactions • Linking candidate behavior to Twitter reactions Vergeer, M., & Franses, P. H. (2015). Live audience responses to live televised election debates: Time series analysis of issue salience and party salience on audience behavior. Information, Communication & Society doi:10.1080/1369118X.2015.1093526 Trilling, D. (2015). Two different debates? Investigating the relationship between a political debate on TV and simultaneous comments on Twitter. Social Science Computer Review, 33(3), 259–276. doi:10.1177/0894439314537886 Yıldırım, A., Üsküdarlı, S., & Özgür, A. (2016). Identifying Topics in Microblogs Using Wikipedia. Plos One, 11(3), e0151885. doi:10.1371/journal.pone.0151885 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  24. 24. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Responses to TV debates Second Screen • Linking events to Twitter reactions • Linking candidate behavior to Twitter reactions Central question How do people react to TV debates? Vergeer, M., & Franses, P. H. (2015). Live audience responses to live televised election debates: Time series analysis of issue salience and party salience on audience behavior. Information, Communication & Society doi:10.1080/1369118X.2015.1093526 Trilling, D. (2015). Two different debates? Investigating the relationship between a political debate on TV and simultaneous comments on Twitter. Social Science Computer Review, 33(3), 259–276. doi:10.1177/0894439314537886 Yıldırım, A., Üsküdarlı, S., & Özgür, A. (2016). Identifying Topics in Microblogs Using Wikipedia. Plos One, 11(3), e0151885. doi:10.1371/journal.pone.0151885 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  25. 25. Example 1: relating word frequencies to each other Trilling, D. (2015). Two different debates? Investigating the relationship between a political debate on TV and simultaneous comments on Twitter. Social Science Computer Review, 33(3), 259–276. doi:10.1177/0894439314537886
  26. 26. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Responses to TV debates A way of visualizing this font size ∼ relative frequency within copus distance to y-axis ∼ log-likelikelihood (= difference between corpora) Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  27. 27. Example 2: manually classify most frequent terms into topics, subsequent time series analysis Vergeer, M., & Franses, P. H. (2015). Live audience responses to live televised election debates: Time series analysis of issue salience and party salience on audience behavior. Information, Communication & Society. doi:10.1080/1369118X.2015.1093526
  28. 28. Example 3: Using external datasource (wikipedia) for topic classification Yıldırım, A., Üsküdarlı, S., & Özgür, A. (2016). Identifying Topics in Microblogs Using Wikipedia. Plos One, 11(3), e0151885. doi:10.1371/journal.pone.0151885
  29. 29. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Incivility Incivility Who uses incivil language on Twitter? Vargo, C. J., & Hopp, T. (2015). Socioeconomic status, social capital, and partisan polarity as predictors of political incivility on Twitter: A congressional district-level analysis. Social Science Computer Review doi:10.1177/0894439315602858 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  30. 30. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Incivility Incivility Who uses incivil language on Twitter? incivility (1) name-calling; (2) threats; (3) vulgarities; (4) abusive or foul language; (5) xenophobia; (6) hateful language, epithets, or slurs; (7) racist or bigoted sentiments; (8) disparaging comments on the basis of race/ethnicity; and (9) use of stereotypes Vargo, C. J., & Hopp, T. (2015). Socioeconomic status, social capital, and partisan polarity as predictors of political incivility on Twitter: A congressional district-level analysis. Social Science Computer Review doi:10.1177/0894439315602858 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  31. 31. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Incivility Incivility Who uses incivil language on Twitter? incivility (1) name-calling; (2) threats; (3) vulgarities; (4) abusive or foul language; (5) xenophobia; (6) hateful language, epithets, or slurs; (7) racist or bigoted sentiments; (8) disparaging comments on the basis of race/ethnicity; and (9) use of stereotypes dictionary approach, based on existing word lists Vargo, C. J., & Hopp, T. (2015). Socioeconomic status, social capital, and partisan polarity as predictors of political incivility on Twitter: A congressional district-level analysis. Social Science Computer Review doi:10.1177/0894439315602858 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  32. 32. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Incivility Incivility The central question Do factors that are thought to be indicators of a functioning democratic discourse (like low polarization) translate to a civil discourse on social media? Vargo, C. J., & Hopp, T. (2015). Socioeconomic status, social capital, and partisan polarity as predictors of political incivility on Twitter: A congressional district-level analysis. Social Science Computer Review doi:10.1177/0894439315602858 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  33. 33. “Our results suggested that uncivil discourse was highest in districts that were characterized, in part, by factors traditionally thought to be indicative of a healthy and diverse democracy (i.e., low levels of partisan polarity and high levels of racial diversity).” “Notably, we failed to either fully or partially support a number of our hypotheses.” Vargo & Hopp, 2015, p. 17
  34. 34. “A number of limitations temper the present findings. First, the nature of the data severely limits the generalizability of our findings. The source of data here, Twitter, is, at best, an instantaneous measure of behavior, not a durable measure of emotion or feelings (Vieweg, 2010). Moreover, Twitter cannot be reasonably understood to be a directly reliable proxy for public opinion in general. Also, the corpus here was limited to a specific event, the 2012 general election. The messages gathered in this analysis were also directed at a specific political candidate (e.g., Obama and/or Romney). While the findings still yield important conclusions toward discourse, democracy, and general elections, we cannot use the current results to make generalizations about the state of political discussion as a whole (either on or off of Twitter).” Vargo & Hopp, 2015, p. 17
  35. 35. Remember: This was just a tiny selection to give you some inspiration about what one can research. There are a bunch of other interesting studies and approaches.
  36. 36. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Further reading Jungherr, A. (2016). Twitter use in election campaigns: A systematic literature review. Journal of Information Technology & Politics, 13(1), 72–91. doi:10.1080/19331681.2015.1132401 Fundamentals of Data Science: Case “Political Communication” Damian Trilling
  37. 37. Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conc Questions? d.c.trilling@uva.nl @damian0604 www.damiantrilling.net Fundamentals of Data Science: Case “Political Communication” Damian Trilling

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