data science: case "political communication 2/2"
TRANSCRIPT
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Fundamentals of Data Science: Case “PoliticalCommunication”
Damian Trilling
[email protected]@damian0604
www.damiantrilling.net
Afdeling CommunicatiewetenschapUniversiteit van Amsterdam
19-09-2016
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
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
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
This week
Digging deeper into the content of the tweets
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
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 tweetsIssuesResponses to TV debatesIncivility
5 Conclusion
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Structure
Analyzing structure vs analyzing content
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
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 whointeracts 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
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
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 whointeracts 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
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
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
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
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
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Content
Systematizing analytical approaches
⇒ It depends on your reserach question which approach ismore interesting!
But probably the most interesting thing is to combine themboth
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Content
Systematizing analytical approaches
⇒ It depends on your reserach question which approach ismore interesting!
But probably the most interesting thing is to combine themboth
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Short sidestep:Agenda setting and Framing
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Agenda setting
Beyond simplistic stimulus-response models of media effects:
Media effects are not so much abouthow we think, but what we thinkaboutMcCombs, 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
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Framing
“To frame is to select some aspects of a perceived reality andmake them more salient in a communicating text, in such a way asto promote a particular problem definition, causal interpretation,moral evaluation, and/or treatment recommendation for the itemdescribed”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
Studies that analyze structure of the Twittersphere
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
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
“In general, famous journalists, experts and politicians are centralactors within the Austrian political Twittersphere and form theirown, dense and influential subnetwork within the broader sphere.Non-professionals may participate in this network, provided thatthey engage receptive members of the elite who act as ‘bridges’between subnetworks. However, when the discussion involvescertain topics, niche authorities emerge, and these authorities –including a few left-wing activists and bloggers – join otherpolitical professionals as central information hubs.”
Ausserhofer & Maireder 2013, p. 19
“While topics such as the financial crisis were massivelyrepresented in the newspapers and on TV, hardly anyone tweetedabout such topics on Twitter. A similar phenomenon could beobserved with the ongoing coverage of corruption-relatedinvestigations, about which only a few users bothered to tweet.Short-living topics such as the aforementioned ball of theright-wing fraternities and the squatting of an abandoned houseand the forced eviction of its ‘residents’ were popular topics onTwitter. A further explanation of why these topics are morepopular on Twitter than in mass media is that activists use theservice not only to discuss but also to facilitate their activities.”
Ausserhofer & Maireder 2013, p. 19
Studies that analyze the content of the tweets
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
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 todetermine who supports whom
• simple keyword searches (dictionary-approach) for topicclassification
• network analysis
Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network Issue Agendas on Twitter During the 2012U.S. Presidential Election. Journal of Communication, 64, 296–316. doi:10.1111/jcom.12089
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
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 todetermine who supports whom
• simple keyword searches (dictionary-approach) for topicclassification
• network analysis
Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network Issue Agendas on Twitter During the 2012U.S. Presidential Election. Journal of Communication, 64, 296–316. doi:10.1111/jcom.12089
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Issues
Networks of issues
• General-interest media issue network predicts issue network ofObama supporters
• Partisan media issue network predicts issue network ofRomney supporters
Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network Issue Agendas on Twitter During the 2012U.S. Presidential Election. Journal of Communication, 64, 296–316. doi:10.1111/jcom.12089
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Responses to TV debates
Second Screen
• Linking events to Twitter reactions• Linking candidate behavior to Twitter reactions
Central questionHow do people react to TV debates?
Vergeer, M., & Franses, P. H. (2015). Live audience responses to live televised election debates: Time seriesanalysis of issue salience and party salience on audience behavior. Information, Communication & Societydoi:10.1080/1369118X.2015.1093526
Trilling, D. (2015). Two different debates? Investigating the relationship between a political debate on TV andsimultaneous 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
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Responses to TV debates
Second Screen
• Linking events to Twitter reactions• Linking candidate behavior to Twitter reactions
Central questionHow do people react to TV debates?
Vergeer, M., & Franses, P. H. (2015). Live audience responses to live televised election debates: Time seriesanalysis of issue salience and party salience on audience behavior. Information, Communication & Societydoi:10.1080/1369118X.2015.1093526
Trilling, D. (2015). Two different debates? Investigating the relationship between a political debate on TV andsimultaneous 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
Example 1:relating word frequencies to each other
Trilling, D. (2015). Two different debates? Investigating the relationship between a political debate on TV andsimultaneous comments on Twitter. Social Science Computer Review, 33(3), 259–276.doi:10.1177/0894439314537886
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Responses to TV debates
A way of visualizing this
font size ∼ relative frequency within copusdistance to y-axis ∼ log-likelikelihood (= difference between corpora)
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
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 seriesanalysis of issue salience and party salience on audience behavior. Information, Communication & Society.doi:10.1080/1369118X.2015.1093526
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
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Incivility
Incivility
Who uses incivil language on Twitter?
incivility(1) name-calling; (2) threats; (3) vulgarities; (4) abusive or foullanguage; (5) xenophobia; (6) hateful language, epithets, or slurs;(7) racist or bigoted sentiments; (8) disparaging comments on thebasis 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 ofpolitical incivility on Twitter: A congressional district-level analysis. Social Science Computer Reviewdoi:10.1177/0894439315602858
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Incivility
Incivility
Who uses incivil language on Twitter?
incivility(1) name-calling; (2) threats; (3) vulgarities; (4) abusive or foullanguage; (5) xenophobia; (6) hateful language, epithets, or slurs;(7) racist or bigoted sentiments; (8) disparaging comments on thebasis 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 ofpolitical incivility on Twitter: A congressional district-level analysis. Social Science Computer Reviewdoi:10.1177/0894439315602858
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Incivility
Incivility
Who uses incivil language on Twitter?
incivility(1) name-calling; (2) threats; (3) vulgarities; (4) abusive or foullanguage; (5) xenophobia; (6) hateful language, epithets, or slurs;(7) racist or bigoted sentiments; (8) disparaging comments on thebasis 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 ofpolitical incivility on Twitter: A congressional district-level analysis. Social Science Computer Reviewdoi:10.1177/0894439315602858
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Incivility
Incivility
The central questionDo factors that are thought to be indicators of a functioningdemocratic discourse (like low polarization) translate to a civildiscourse on social media?
Vargo, C. J., & Hopp, T. (2015). Socioeconomic status, social capital, and partisan polarity as predictors ofpolitical incivility on Twitter: A congressional district-level analysis. Social Science Computer Reviewdoi:10.1177/0894439315602858
Fundamentals of Data Science: Case “Political Communication” Damian Trilling
“Our results suggested that uncivil discourse was highest indistricts that were characterized, in part, by factors traditionallythought 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 ofour hypotheses.”
Vargo & Hopp, 2015, p. 17
“A number of limitations temper the present findings. First, thenature of the data severely limits the generalizability of ourfindings. The source of data here, Twitter, is, at best, aninstantaneous measure of behavior, not a durable measure ofemotion or feelings (Vieweg, 2010). Moreover, Twitter cannot bereasonably understood to be a directly reliable proxy for publicopinion in general. Also, the corpus here was limited to a specificevent, the 2012 general election. The messages gathered in thisanalysis were also directed at a specific political candidate (e.g.,Obama and/or Romney). While the findings still yield importantconclusions toward discourse, democracy, and general elections, wecannot use the current results to make generalizations about thestate of political discussion as a whole (either on or off of Twitter).”
Vargo & Hopp, 2015, p. 17
Remember:
This was just a tiny selection to give you some inspirationabout what one can research.
There are a bunch of other interesting studies and approaches.
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Further reading
Jungherr, A. (2016). Twitter use in election campaigns: Asystematic 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
Analyzing structure vs analyzing content Short sidestep: Agenda setting and Framing Structure Content Conclusion
Questions?
[email protected]@damian0604
www.damiantrilling.net
Fundamentals of Data Science: Case “Political Communication” Damian Trilling