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Acoustic features of dysphonic speech vs normal speech in New Zealand English speakers
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| 83 |
Modeling verb valency in a computational grammar for Portuguese in the HPSG formalism ; Modelação da valência verbal numa gramática computacional do português no formalismo HPSG
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In: Domínios de Lingu@gem; Ahead of Print; 1-63 ; 1980-5799 (2022)
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| 84 |
Causal and Semantic Relations in L2 Text Processing: An Eye-Tracking Study
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Nahatame, Shingo. - : University of Hawaii National Foreign Language Resource Center, 2022. : Center for Language & Technology, 2022
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| 85 |
Recognition of Urdu sign language: a systematic review of the machine learning classification
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In: PeerJ Comput Sci (2022)
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| 86 |
Multi-label emotion classification of Urdu tweets
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In: PeerJ Comput Sci (2022)
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| 87 |
Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache
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| 88 |
Word Frequency Analysis of Community Reaction to Religious Violence on Social Media
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In: School of Computer Science & Engineering Faculty Publications (2022)
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| 89 |
(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment
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In: PeerJ Comput Sci (2022)
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Abstract:
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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Keyword:
Computational Linguistics
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URL: https://doi.org/10.7717/peerj-cs.947
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044321/
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| 90 |
A systematic literature review on spam content detection and classification
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In: PeerJ Comput Sci (2022)
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| 91 |
People’s expectations and experiences of big data collection in the Saudi context
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In: PeerJ Comput Sci (2022)
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| 92 |
Developing and evaluating cybersecurity competencies for students in computing programs
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In: PeerJ Comput Sci (2022)
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| 93 |
Multitask Pointer Network for Multi-Representational Parsing
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| 94 |
CorpusExplorer ; Eine Software zur korpuspragmatischen Analyse
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| 95 |
Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 96 |
Masked language models directly encode linguistic uncertainty
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 97 |
Learning Stress Patterns with a Sequence-to-Sequence Neural Network
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 98 |
Modeling human-like morphological prediction
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 99 |
The interaction between cognitive ease and informativeness shapes the lexicons of natural languages
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In: Proceedings of the Society for Computation in Linguistics (2022)
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| 100 |
What is so Plautine about Plautine Language? Computers and the Style of Early Latin Drama
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In: Peter Barrios-Lech (2022)
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