Libby Hemphill, "Elected Officials and Social Media"


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  • DW-NOMINATE Poole and Rosenthal Calculated for all Congresses Multi-dimensional scaling method of predicting how a person will vote First dimension = liberal/conservative Second dimension = cultural/lifestyle issues “ polarized” voters are far from 0
  • What you can get done on a small campus with limited money and no students is different from what you can get done as part of a well-funded group on a well-funded campus Found data has dangers like Twitter’s changing API TOS You don’t have to be all about open data to benefit from keeping track of your metadata – your “next week self” will need to know that stuff You don’t need to study everything RIGHT NOW Guardian asks whether Twitter is a leftwing mob Interest in politics and news did not increase adoption of Twitter Position yourself outside your home discipline - esp when you have no home discipline to begin with Future question - how do citizens perceive government's use of social media? jumping into something like public officials on social media opens you to a lot of "well, i Know" and "in my experience", even your own, so you need a thick skin Scale is interesting, but saying "Twitter is" is like saying "Everyone with a cell phone" or "Everyone with a laptop", it just doesn't make sense
  • Libby Hemphill, "Elected Officials and Social Media"

    1. 1. ELECTED OFFICIALSON SOCIAL MEDIALibby Hemphill, PhDAssistant Professor of Communication andInformation StudiesIllinois Institute of
    2. 2. Questions from Group• What’s the bias on Twitter?• Are Republicans more tightly connected?• - What do they do in free time? (Which bars do they go to?) - Where are they tweeting from? (location) - What do they respond to? - How engaging are they (tweets, not officials?) - Tweet-content versus voting record - What lobbyists/special interest groups do they tweet about/follow/etc? - How much do they interact (via tweets) with outside groups - How does their tweeting correlate to offline activity (eg polls) - Is Twitter it (for social media), eg., #fb - What is the sentiment of their posts - Diffs between Individual vs. party posts - How many people/Who do they follow - Adoption (Are they/how are they? they like it?) - demographics of their followers - differences between "official" versus campaign vs personal (accounts/how many, what kind) - How many tweets are "spontaneous vs crafted" - How do talking points change/evolve over time? - How does their language change/evolve over time? - How many are bilingual (what languages?) - What do they misspell? - Do they like (twitter) or not? - How frequently are they re-tweeted and how far do they go (depth/diffusion) - Which lobbyists/special interest groups do they spend time with? - How does their language (tweet word choice) vary from official statements - What pics do they post about themselves - How do they frame their issues? (lang/sentiment/etc) - How do people respond the their tweets? - What is their “agenda” - What devices do they use to tweet
    3. 3. I wonder…• Whether officials are always talking about their next TV appearance• Whether officials use Twitter to get people to do something like give money or help out at a community event• Whether national and local officials use Twitter differently• Who tweets with their officials• How what we and our officials do on Twitter effects the “real” world• If Twitter is a virtual echo chamber in which officials interact mainly with themselves• How officials’ use RTs, mentions, and hashtags• How much of this stuff is unique to Americans? Or Chicagoans? Or Republicans• Are officials really tweeting, or is it all staffers in a post-Weiner era• How what they’re talking about Twitter differs from what they’re talking about elsewhere like MSNBC or Fox News appearances• What clues the language they use gives us about how they’ll vote• How the language they use influences what we do with their tweets
    4. 4. Technology Toolkit Getting and Storing Data Processing and Analyzing Data• MySQL • MALLET• MongoDB • NodeXL• PHP • UCINet• Ruby •R• Python • Stata• Perl • Excel• Dropbox • Word• Github • PowerPoint• Amazon Web Services
    5. 5. Why so many?• Students come in with different skills• Existing tools focus on topics, but we focus on people• Ongoing data collection• Different representations for different outlets• Two-mode networks have different requirements
    6. 6. Datasets “Mine” Re-used• U.S. Congress • Twitter Streaming API • Speech acts • DW-NOMINATE • Mentioning each other • Hashtags (one-mode, • two-mode) • Fowler’s Co-sponsorship • Links (one-mode, two-mode) • Census and American• Korean National Community Survey Assembly • Speech acts •• Members of the EU Parliament (coming soon)
    7. 7. Takeaways• Resources matter• Found data can be sexy but dangerous• Keep track of the steps you took between idea and paper (~= metadata)• Think long term about your work
    8. 8. More Info• Email:• Twitter: @libbyh• Web: and