1. Tromble, Rebekah. “Thanks for (actually) Responding! How Citizen Demand Shapes Politicians’ Interactive Practices on Twitter.” New Media & Society. 20.2 (2018): 676-697. Web.
In this article Tromble used the results of a random sample of tweets from and directed at lower level politicians in the Netherlands, the UK, and the US during the latter half of October 2013 to investigate the how “citizen demand” (direct requests from citizens on social media) affect how politicians engage with citizens via social media, with specific interest in the “reciprocity” (the idea that certain responses, positive, negative, or otherwise, beget similar responses). She found that there was a discrepancy between the amount of reciprocal interactions, AKA real conversations between politicians and citizens, in the Netherlands and the UK when compared to the US, with the US having significantly less reciprocal interactions. This coincided with more negatively-toned initial requests from US citizens to politicians as opposed to the Netherlands and the UK, which Tromble suggests may be part of the cause for such low reciprocal interaction, although she also proposes that the traditional “top-down” style of information dispersal of politicians, as used in public addresses, is likely also involved. This article is useful for my project because it addresses how politicians interact with citizens via social media and how their use of social media might be affected by citizen interactions, which coincides with my own interest in investigating how politicians interact with their constituents on social media. However, as most of the tweets were hand coded, rather than automatically coded as I hope to do with mine, my own project is focused solely on the US, and because Tromble is not investigating specifically linguistic data like syntax or phrase structure, this study is mainly useful as context for my own study.
2. Azmi, Alia. Sylvia, Ike. Mardhiah, Desy. “Discourse Analysis of Politicians’ Social Media Posts.” Jurnal the Messenger. 10.2 (2018): 174-186. Web.
In this article Azami et al. use discourse analysis to examine the social media posts of three political figures with the most followers on three social media platforms (Facebook, Instragram, and Twitter) to understand the explicit and implicit political messages in their posts. They analyzed written texts and other content (photos/cartoons and videos) taken from August to September 2017 on each politician’s page (only the most followed one). They found that each politician has their own “style” of posting that not only sets them apart from other politicians but also implicitly conveys their own political and social beliefs to their followers and how they seem to try to cater their posts to forward their own endeavors (i.e. greeting citizens on a religious holiday to garner favor/build rapport). Although the focus of this study was Indonesian politicians, it is still a useful resource for my project. The fact that it examines the limitations of the messages one can make on different social media platforms and focuses on the individual style (including linguistic style) of each politician will make it useful for comparison when I am trying to determine how much of the variation of Twitter use by the politicians I study is due to platform restraints and how much is due to personal “style”. It also discusses the use of different registers of language, from formal to informal to oral, which is something that I would also like to examine in my own research.
3. Tromble, Rebekah. “The Great Leveler? Comparing Citizen-politician Twitter Engagement across Three Western Democracies.” European Political Science : EPS. 17.2 (2018): 223-239. Web.
This study follows up on the other Tromble study discussed above by using the same random sample of tweets to investigate the types of people that politicians have “reciprocal interactions” with and what this says about said politicians/how well social media really “levels” the playing field to allow ordinary citizens to interact with politicians where they traditionally couldn’t. For this study interlocutors were coded into seven categories: political, media, celebrity, business, interest group, other institutional (institutionally based identities such as religious groups or academic institutions), and citizens, with interlocutors falling into more than one category being coded as the “highest” category possible. She found that ideologically left parties talked to citizens more, while ideologically right parties talked to businesses more. She also found that, for the US and the UK, of all interactions about 1/4 were with citizens, 1/4 were with political entities, and about 1/5 were with interest groups (the Netherlands a lot more interactions with political entities, even though citizen interactions were also high), and that the size of political party was also a factor (bigger = more elite interactions). This study is useful because it acknowledges and investigates the social strata of social media users and how one’s social “status” can affect how one interacts with others and is interacted with by others of the same or differing “statuses”, which is a variable I wish to investigate in my own research. Their coding standards for the different categories of users will also be useful both in forming my own categories and in using their data for comparison to my own (at least their US data).
4. Cook, James M. “Twitter Adoption and Activity in U.S. Legislatures: A 50-State Study.” The American Behavioral Scientist. 61.7 (2017): 724-740. Web.
In this study Cook examines the factors affecting Twitter adoption at the state legislative level and the affect of certain factors (age, gender, state, district, party, status as legislative “veteran”/”leader”, education, median district income, median district age, etc.) on this adoption and active use using statistical analysis. He found that the significant variables for Twitter adoption often varied from state to state, although median district education level seemed to be a more stable predictor. Overall, he found that factors such as “constituents per legislator, youth and educational attainment of a district, legislative professionalism, being a woman, sitting in the upper chamber, leadership, and legislative experience” seemed to be linked to a legislator being more likely to adopt the use of Twitter. This article is useful for my project because it gives me context for examining what social/contextual factors may have an impact on how comfortable/widespread the politicians that I am studying are with Twitter, and gives me more insight into what factors I want to control for when selecting the politicians that I want to study.
5. Diana Evans Yiannakis. “House Members’ Communication Styles: Newsletters and Press Releases.” The Journal of Politics, vol. 44, no. 4, 1982, p. 1049. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&db=edsjsr&AN=edsjsr.2130673&site=eds-live. Web.
In this article Yiannakis investigates the communications styles that politicians (specifically House of Representatives members) use to address their citizens and how district and individual characteristics affect their style by examining the content of newsletters and press releases produced during the 94th and six months of the 95th Congresses. They took a random sample of 30 members of the House of Representatives and focused on their newsletters and press releases. She found that the House members’ communication style was greatly influenced by their “political circumstances”, with politicians tending to cater their addresses so that they lined up with what their constituents wanted to hear/what they thought they wanted to hear. Their style also seemed to be influenced by ideology and influence, with more ideological members being inconsistent in whether they claim credit for outcomes catering to their constituents and with more senior House members being more likely to claim credit for such outcomes. This article is useful as it gives me more insight into how politicians interact with their constituents and what factors might affect how they interact. It is also useful as it gives me a more “traditional” form of address to compare to the Twitter data that I collect, to see the extent of the effect of the medium of address (if there is any) on the style politicians choose to use. I can also compare the uses of newsletters and press releases to the uses of tweets and see if they are used for similar purposes or if politicians seem to tailor their medium to fit the purpose. Unfortunately, since this article was not written recently (not even in the past decade), I can at most use this as context for my own inferences and use it to help guide my study of similar forms of media use in the present day.
6. Golbeck, Jennifer. Auxier, Brooke. Bickford, Abigail. Cabrera, Lautaro. Conte McHugh, Meaghan. Moore, Stephani. Hart, Jacquelyn. Resti, Justin. Rogers, Anthony. Zimmerman, Jenna. “Congressional Twitter Use Revisited on the Platform’s 10‐year Anniversary.” Journal of the Association for Information Science and Technology. 69.8 (2018): 1067-1070. Web.
In this article Golbeck et al. compared tweets from a 2009 study and tweets from early 2017 to examine how the tweeting habits of Congress members had changed over time. They found that, although only 159 members of Congress had Twitter accounts in 2009, every Congress member had a Twitter account in 2017. They also found that, as a whole, Congress members seem to be tweeting about the same things, although they are tweeting more in general. Golbeck et al. also found that most of the tweets examined were either meant to convey pertinent information (either about political stances/issues or about events) or featured the Congress member at some activity, usually related to politics. Aside from investigating what the Congress members were tweeting about, Golbeck et al. also mapped how “connected” each member was to other members of Congress (who they followed on Twitter), which shows a clear party bias. This article is helpful because it provides background for some of the major ways that politicians seem to be using Twitter, as well as providing some insight into how politicians use Twitter to connect to their fellow politicians.
7. McGregor, Shannon C. Lawrence, Regina G. Cardona, Arielle. “Personalization, Gender, and Social Media: Gubernatorial Candidates’ Social Media Strategies.” Information, Communication and Society. 20.2 (2017): 264-283. Web.
In this study McGregor et al. explore how politicians use “self-personalization” (posting about their personal lives on social media) as a strategy to get their constituents to feel more connected to them, and how they use social media to do it, with a specific focus on the effect of gender on these strategies. They did this by examining the social media posts of 18 gubernatorial candidates in 2014 posted between January 1st and November 5th. They found that men seem to engage in more “self-personalization” than their female opponents, although female candidates in competitive races seem to have used “self-personalization” more than other female candidates. Tighter races seemed to increase male “self-personalization” as well. This article is useful because it provides context on how gender and gender stereotypes might effect how politicians use Twitter, which will help me when I am conducting my own research.
8. “Relationships Among Twitter Conversation Networks, Language Use, and Congressional Voting.” Conference Papers — International Communication Association, 2012 Annual Meeting 2012, pp. 1–24. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&db=ufh&AN=85900300&site=eds-live. Web.
In this paper the authors examine relationships between “conversation networks, language use, and political behavior” by looking at the Twitter activity of 411 Congress members from June 14th to August 23rd 2011. They are especially interested in how the Congress members use Twitter, interact with each other in real life, and vote are related. They then mapped out the “connections” they found between members based on follows and mentions, which showed the two parties with a pretty clear divide between them. They also found that Republicans seem to use Twitter for communication amongst party members than Democrats, who mostly just follow their colleagues. They also found that Congress members seem to be using Twitter to “implicitly campaign”, as they do in other types of media (speeches, websites, etc.). This article is useful because it gives me insight into how politicians’ use of Twitter relates to how they use other forms of media, as well as more insight into how they use Twitter to “connect” with and communicate with their colleagues.
9. Salloum, Said A., Al-Emran, Mostafa. Shaalan, Khaled. “A survey of text mining in social media: facebook and twitter perspectives.” Adv. Sci. Technol. Eng. Syst. J 2.1 (2017): 127-133.
In this article Salloum et al. give a survey of text mining/data mining techniques specifically centered on the use of data from social media (social media posts) platforms such as Facebook and Twitter. These techniques include text clustering, text categorization, association rule extraction and trend analysis. This article is useful because it helps familiarize me with the different methods of text/data mining and analysis that I could potentially use to gather my project data, as well as giving me a jumping off point should I need to find/refine a different method of data mining.
10. Brezina, Vaclav. “Sociolinguistics and Stylistics: Individual and Social Variation.” Statistics in Corpus Linguistics: A Practical Guide. Cambidge: Cambridge UP, 2018. 183-218. Print.
In this chapter several statistical techniques for analyzing stylistic and sociolinguistic variations in corpora are described, such as the t-test, ANOVA, the Mann-Whitney U test, the Kruskal-Wallis test, and mixed effects models. This chapter is useful because it familiarizes me with several methods that I can use to analyze my data on politicians and see what patterns of use I can find. It is also useful because it elaborates on how to specifically investigate matters of linguistic style and sociolinguistic influences, which are two areas that I want to focus my research around.