Trump’s Impeachment: Cheap Grace and the Comic Frame

Milford, Mike

doi:10.1080/1041794x.2021.1925952

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Such studies are now covered by dedicated volumes ( Kamps, 2021 ; Schier, 2022 ) as well as a wealth of scholarly works, including large-scale social network analysis and mining ( Zheng et al, 2021 ), analyses of media responses ( Christenson, Kreps & Kriner, 2021 ), the interplay of tweets and media ( Morales, Schultz & Landreville, 2021 ), or the (absence of) impact between his tweets and stock returns ( Machus, Mestel & Theissen, 2022 ). Related studies also include analyses about comments from political figures about Trump ( Milford, 2021 ; Alexandre, Jai-sung Yoo & Murthy, 2021 ) and vice versa ( Brown Crosby, 2022 ), analyses of his letter during the first impeachment ( Reyes & Ross, 2021 ), and detailed examinations about how Trump’s message was perceived by specific groups such as White extremists ( Long, 2022 ). The impeachment and handling of COVID-19 have received particular attention on Twitter ( Dejard et al, 2021 ; Cervi, García & Marín-Lladó, 2021 ), as key moments of his presidency.…”

Section: Introductionmentioning

confidence: 99%

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

Galgoczy

Phatak

Vinson

et al. 2022

PeerJ Computer Science

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Influencing and framing debates on Twitter provides power to shape public opinion. Bots have become essential tools of ‘computational propaganda’ on social media such as Twitter, often contributing to a large fraction of the tweets regarding political events such as elections. Although analyses have been conducted regarding the first impeachment of former president Donald Trump, they have been focused on either a manual examination of relatively few tweets to emphasize rhetoric, or the use of Natural Language Processing (NLP) of a much larger corpus with respect to common metrics such as sentiment. In this paper, we complement existing analyses by examining the role of bots in the first impeachment with respect to three questions as follows. (Q1) Are bots actively involved in the debate? (Q2) Do bots target one political affiliation more than another? (Q3) Which sources are used by bots to support their arguments? Our methods start with collecting over 13M tweets on six key dates, from October 6th 2019 to January 21st 2020. We used machine learning to evaluate the sentiment of the tweets (via BERT) and whether it originates from a bot. We then examined these sentiments with respect to a balanced sample of Democrats and Republicans directly relevant to the impeachment, such as House Speaker Nancy Pelosi, senator Mitch McConnell, and (then former Vice President) Joe Biden. The content of posts from bots was further analyzed with respect to the sources used (with bias ratings from AllSides and Ad Fontes) and themes. Our first finding is that bots have played a significant role in contributing to the overall negative tone of the debate (Q1). Bots were targeting Democrats more than Republicans (Q2), as evidenced both by a difference in ratio (bots had more negative-to-positive tweets on Democrats than Republicans) and in composition (use of derogatory nicknames). Finally, the sources provided by bots were almost twice as likely to be from the right than the left, with a noticeable use of hyper-partisan right and most extreme right sources (Q3). Bots were thus purposely used to promote a misleading version of events. Overall, this suggests an intentional use of bots as part of a strategy, thus providing further confirmation that computational propaganda is involved in defining political events in the United States. As any empirical analysis, our work has several limitations. For example, Trump’s rhetoric on Twitter has previously been characterized by an overly negative tone, thus tweets detected as negative may be echoing his message rather than acting against him. Previous works show that this possibility is limited, and its existence would only strengthen our conclusions. As our analysis is based on NLP, we focus on processing a large volume of tweets rather than manually reading all of them, thus future studies may complement our approach by using qualitative methods to assess the specific arguments used by bots.

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