The Dynamics of Peer-Produced Political Information During the 2016 U.S. Presidential Campaign

Wikipedia plays a crucial role for online information seeking and its editors have a remarkable capacity to rapidly revise its content in response to current events. How did the production and consumption of political information on Wikipedia mirror the dynamics of the 2016 U.S. Presidential campaign? Drawing on systems justification theory and methods for measuring the enthusiasm gap among voters, this paper quantitatively analyzes the candidates’ biographical and related articles and their editors. Information production and consumption patterns match major events over the course of the campaign, but Trump-related articles show consistently higher levels of engagement than Clinton-related articles. Analysis of the editors’ participation and backgrounds show analogous shifts in the composition and durability of the collaborations around each candidate. The implications for using Wikipedia to monitor political engagement are discussed

Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign

Until recently, social media was seen to promote democratic discourse on social and political issues. However, this powerful communication platform has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress' investigation of Russian interference in the 2016 U.S. election campaign, with Russia accused of using trolls (malicious accounts created to manipulate) and bots to spread misinformation and politically biased information. In this study, we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset with over 43 million election-related posts shared on Twitter between September 16 and October 21, 2016, by about 5.7 million distinct users. This dataset included accounts associated with the identified Russian trolls. We use label propagation to infer the ideology of all users based on the news sources they shared. This method enables us to classify a large number of users as liberal or conservative with precision and recall above 90%. Conservatives retweeted Russian trolls about 31 times more often than liberals and produced 36x more tweets. Additionally, most retweets of troll content originated from two Southern states: Tennessee and Texas. Using state-of-the-art bot detection techniques, we estimated that about 4.9% and 6.2% of liberal and conservative users respectively were bots. Text analysis on the content shared by trolls reveals that they had a mostly conservative, pro-Trump agenda. Although an ideologically broad swath of Twitter users was exposed to Russian Trolls in the period leading up to the 2016 U.S. Presidential election, it was mainly conservatives who helped amplify their message

“Software agents and haunted media : the twitter bot as political actor"

This report examines the rhetorical construction of Twitter bots as nonhuman political agents in press coverage of the 2016 U.S. election. It takes the rhetorical framing of “the Twitter bot” as a case study to argue that Twitter bots are a contemporary example of what media historian Jeffrey Sconce calls “haunted media” -- a communication technology that has been culturally ascribed an “uncanny” “agency.” First, this report provides a comparative close reading of two pieces from The Atlantic and The New York Times as examples of mainstream press coverage of bots shortly before and after the 2016 U.S. presidential election. Second, drawing on Sconce’s analysis of nineteenth and twentieth century media ecologies, it argues that “the Twitter bot” has been rhetorically constructed as haunted media through discourses that are inseparable from larger political narratives. The third and final section speculates on possible theoretical frameworks to expand this project in further inquiries. This report aims to demonstrate that haunted media narratives predate and persist beyond a specific election cycle or medium, and to argue that the construction of “haunted media” occurs alongside constructed concepts of democracy in our technologically mediated society. In doing so, this report contributes to the field of rhetoric of digital technology by bringing it further into conversation with political rhetoric.Englis

From the bargaining table to the ballot box: political effects of right to work laws

Labor unions play a central role in the Democratic party coalition, providing candidates with voters, volunteers, and contributions, as well as lobbying policymakers. Has the sustained decline of organized labor hurt Democrats in elections and shifted public policy? We use the enactment of right-to-work laws—which weaken unions by removing agency shop protections—to estimate the effect of unions on politics from 1980 to 2016. Comparing counties on either side of a state and right-to-work border to causally identify the effects of the state laws, we find that right-towork laws reduce Democratic Presidential vote shares by 3.5 percentage points. We find similar effects in US Senate, US House, and Gubernatorial races, as well as on state legislative control. Turnout is also 2 to 3 percentage points lower in right-to-work counties after those laws pass. We next explore the mechanisms behind these effects, finding that right-to-work laws dampen organized labor campaign contributions to Democrats and that potential Democratic voters are less likely to be contacted to vote in right-to-work states. The weakening of unions also has large downstream effects both on who runs for office and on state legislative policy. Fewer working class candidates serve in state legislatures and Congress, and state policy moves in a more conservative direction following the passage of right-to-work laws

Beyond Partisanship: Outperforming the Party Label with Local Roots in Congressional Elections

While factors like partisanship are increasingly decisive in congressional elections, they do not fully explain variation in constituency support between similarly situated incumbents. I argue that legislators’ reelection success is also influenced by the depth of their local, pre-Congress roots in the district they represent. I theorize that this local connection offers practical advantages to incumbents, such as built-in grassroots political infrastructure in their districts. Shared local identity also allows legislators to relate to their voters on a dimension that is uniquely suited to cross-cut partisanship and qualify them to represent their particular constituents. Therefore, I argue that local roots outperform their district’s partisan expectations – and more specifically, their party’s presidential nominees. Using an original dataset of nearly 3,000 House incumbents from 2002 to 2018 and novel measures of their preexisting local roots in their districts, I find that deeply rooted incumbents outperform their party’s presidential nominees in their districts by an average of about five additional points, even after controlling for partisanship and other crucial factors. I also find that this impact grows as the depth of local roots among a district’s voters increases. These results indicate that even in an era of congressional politics largely defined by partisanship and presidential loyalty, dyadic district connections like local ties can break through and affect legislators’ standing among their constituents

Race and the Race for the White House: On Social Research in the Age of Trump

As it became clear that Donald Trump had a real base of political support, even as analysts consistently underestimated his electoral prospects, they grew increasingly fascinated with the question of who was supporting him (and why). However, researchers also tend to hold strong negative opinions about Trump. Consequently, they have approached this research with uncharitable priors about the kind of person who would support him and what they would be motivated by. Research design and data analysis often seem to be oriented towards reinforcing those assumptions. This essay highlights the epistemological consequences of these tendencies through a series of case studies featuring prominent and influential works that purport to explain the role of race and racism in the 2016 U.S. presidential election. It demonstrates that quality control systems, which should catch major errors, seem to be failing in systematic ways as a result of shared priors and commitments between authors, reviewers and editors – which are also held in common with the journalists and scholars citing and amplifying this work – leading to misinformation cascades. Of course, motivated reasoning, confirmation bias, prejudicial study design, and failure to address confounds are not limited to questions about Trump – however they seem to be particularly pronounced in this case due to the relative homogeneity and intensity of scholars’ views about this topic as compared to other social phenomena. “Trump studies,” therefore, provides fertile ground for exploring how social research can go awry – and the consequences of these failures -- particularly with respect to work on contentious and politically-charged topics