Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy

Ps, Dodds; Minot,; Arnold, MV; Alshaabi, Thayer; Jl, Adams; Aj, Reagan; Cm, Danforth

doi:10.1371/journal.pone.0260592

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…In any case, detected stories are separate contexts which could otherwise be considered the same topic. For example, story detection applied to Donald Trump's twitter timeline can distinguish withinparty arguments from between-party arguments, which both belong to the topic of federal US politics (Dodds et al 2021). Another example applies story detection to the Twitter discussion following the police killing of Michael Brown (Srijith et al 2017).…”

Section: Related Workmentioning

confidence: 99%

Contextualizing Online Conversational Networks

Magelinski¹,

Carley²

2023

ICWSM

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Online social connections occur within a specific conversational context. Prior work in network analysis of social media data attempts to contextualize data through filtering. We propose a method of contextualizing online conversational connections automatically and illustrate this method with Twitter data. Specifically, we detail a graph neural network model capable of representing tweets in a vector space based on their text, hashtags, URLs, and neighboring tweets. Once tweets are represented, clusters of tweets uncover conversational contexts. We apply our method to a dataset with 4.5 million tweets discussing the 2020 US election. We find that even filtered data contains many different conversational contexts, with users engaging in multiple conversations. While users engage in multiple conversations, the overlap between any two pairs of conversations tends to be only 30-40%, giving very different networks for different conversations. Even accounting for this variation, we show that the relative social status of users varies considerably across contexts, with tau=0.472 on average. Our findings imply that standard network analysis on social media data can be unreliable in the face of multiple conversational contexts.

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Section: Related Workmentioning

confidence: 99%

Contextualizing Online Conversational Networks

Magelinski¹,

Carley²

2023

ICWSM

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“…Our methodology (for details, see “ Materials and methods ”) draws on a long history of research on the language of politics and its function in democracies 15 – 18 . For instance, prior work has used records of spoken and written political language to establish the prevalence of negative language among political extremists 19 ; to quantify growing partisanship and polarization 20 , as well as displayed happiness 21 , among US Congress members; to analyze political leaders’ psychological attributes such as certainty and analytical thinking 22 ; to quantify the turbulence of Trump’s presidency 23 ; or to measure the effect of linguistic features on the success of US presidential candidates 24 and on public approval of US Congress 25 . A combination of political discourse analysis and psychological measurement tools has further been applied to obtain insights into the personality traits and sentiments of politicians in general 26 , 27 , as well Donald Trump in particular 28 – 31 .…”

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confidence: 99%

United States politicians’ tone became more negative with 2016 primary campaigns

Jonathan

Spitz

Abu‐Akel

et al. 2023

Sci Rep

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There is a widespread belief that the tone of political debate in the US has become more negative recently, in particular when Donald Trump entered politics. At the same time, there is disagreement as to whether Trump changed or merely continued previous trends. To date, data-driven evidence regarding these questions is scarce, partly due to the difficulty of obtaining a comprehensive, longitudinal record of politicians’ utterances. Here we apply psycholinguistic tools to a novel, comprehensive corpus of 24 million quotes from online news attributed to 18,627 US politicians in order to analyze how the tone of US politicians’ language as reported in online media evolved between 2008 and 2020. We show that, whereas the frequency of negative emotion words had decreased continuously during Obama’s tenure, it suddenly and lastingly increased with the 2016 primary campaigns, by 1.6 pre-campaign standard deviations, or 8% of the pre-campaign mean, in a pattern that emerges across parties. The effect size drops by 40% when omitting Trump’s quotes, and by 50% when averaging over speakers rather than quotes, implying that prominent speakers, and Trump in particular, have disproportionately, though not exclusively, contributed to the rise in negative language. This work provides the first large-scale data-driven evidence of a drastic shift toward a more negative political tone following Trump’s campaign start as a catalyst. The findings have important implications for the debate about the state of US politics.

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Twitter misogyny associated with Hillary Clinton increased throughout the 2016 U.S. election campaign

Weaving

Alshaabi

Arnold

et al. 2023

Sci Rep

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Online misogyny has become a fixture in female politicians’ lives. Backlash theory suggests that it may represent a threat response prompted by female politicians’ counterstereotypical, power-seeking behaviors. We investigated this hypothesis by analyzing Twitter references to Hillary Clinton before, during, and after her presidential campaign. We collected a corpus of over 9 million tweets from 2014 to 2018 that referred to Hillary Clinton, and employed an interrupted time series analysis on the relative frequency of misogynistic language within the corpus. Prior to 2015, the level of misogyny associated with Clinton decreased over time, but this trend reversed when she announced her presidential campaign. During the campaign, misogyny steadily increased and only plateaued after the election, when the threat of her electoral success had subsided. These findings are consistent with the notion that online misogyny towards female political nominees is a form of backlash prompted by their ambition for power in the political arena.

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Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy

Cited by 7 publications

References 42 publications

Contextualizing Online Conversational Networks

Contextualizing Online Conversational Networks

United States politicians’ tone became more negative with 2016 primary campaigns

Twitter misogyny associated with Hillary Clinton increased throughout the 2016 U.S. election campaign

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