Identifying the sentiment styles of YouTube’s vloggers

Kleinberg, Bennett; Mozes, Maximilian; Vegt, Isabelle van der

doi:10.18653/v1/d18-1394

Cited by 12 publications

(21 citation statements)

References 19 publications

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“…Sentiment extraction: Finally, we measured each post's sentiment using a sentiment look-up table in an algorithm that considered valence shifters (e.g., "hardly", "not") of the sentiment values. Valence shifters can change the polarity of a sentiment ("don't like" vs "like") or amplify ("really bad" vs "bad") and de-amplify ("barely exciting" vs "exciting") a sentiment [25,26]. Specifically, we built a context window of two words before and after a sentiment match and corrected the sentiment if valence shifters were present in the resulting 5-word context window.…”

Section: Modelling Extremist Languagementioning

confidence: 99%

“…Trajectory extraction: The individual temporal trajectories for the posts made on the forum per user were obtained by standardising the temporal progression to a scale from 1 to 100, and by applying a discrete cosine transformation to the post frequency scores [25,26,33]. 3 The forum engagement was scaled to a score ranging from 0 (minimal forum engagement) to 1 (maximal forum engagement).…”

Section: User-level Analysismentioning

confidence: 99%

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The temporal evolution of a far-right forum

2020

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The increased threat of right-wing extremist violence necessitates a better understanding of online extremism. Radical message boards, small-scale social media platforms, and other internet fringes have been reported to fuel hatred. The current paper examines data from the right-wing forum Stormfront between 2001 and 2015. We specifically aim to understand the development of user activity and the use of extremist language. Various time-series models depict posting frequency and the prevalence and intensity of extremist language. Individual user analyses examine whether some super users dominate the forum. The results suggest that structural break models capture the forum evolution better than stationary or linear change models. We observed an increase of forum engagement followed by a decrease towards the end of the time range. However, the proportion of extremist language on the forum increased in a step-wise matter until the early summer of 2011, followed by a decrease. This temporal development suggests that forum rhetoric did not necessarily become more extreme over time. Individual user analysis revealed that super forum users accounted for the vast majority of posts and of extremist language. These users differed from normal users in their evolution of forum engagement.

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Section: Modelling Extremist Languagementioning

confidence: 99%

Section: User-level Analysismentioning

confidence: 99%

The temporal evolution of a far-right forum

2020

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“…The method for retrieving YouTube video transcripts follows the procedure of related research [49,50]. To retrieve the transcripts, a Python script was written using www.downs ub.com to obtain XML-encoded transcripts.…”

Section: Transcript Retrievalmentioning

confidence: 99%

Online influence, offline violence: language use on YouTube surrounding the ‘Unite the Right’ rally

et al. 2020

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The media frequently describes the 2017 Charlottesville ‘Unite the Right’ rally as a turning point for the alt-right and white supremacist movements. Social movement theory suggests that the media attention and public discourse concerning the rally may have engendered changes in social identity performance and visibility of the alt-right, but this has yet to be empirically tested. The presence of the movement on YouTube is of particular interest, as this platform has been referred to as a breeding ground for the alt-right. The current study investigates whether there are differences in language use between 7142 alt-right and progressive YouTube channels, in addition to measuring possible changes as a result of the rally. To do so, we create structural topic models and measure bigram proportions in video transcripts, spanning approximately 2 months before and after the rally. We observe differences in topics between the two groups, with the ‘alternative influencers’, for example, discussing topics related to race and free speech to a larger extent than progressive channels. We also observe structural breakpoints in the use of bigrams at the time of the rally, suggesting there are changes in language use within the two groups as a result of the rally. While most changes relate to mentions of the rally itself, the alternative group also shows an increase in promotion of their YouTube channels. In light of social movement theory, we argue that language use on YouTube shows that the Charlottesville rally indeed triggered changes in social identity performance and visibility of the alt-right.

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“…The organizers of the workshop provided us with the necessary features for further analyses, who were inspired by Jockers (2015) and Gao et al (2016); (for more details see Kleinberg et al (2018)). Features were generated from the transcripts, which capture the sentiment change throughout the transcript.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The applied method is based on the approach of the R package "sentimentr" (Rinker, 2019a), which generates sentiments on a sentence level, but the current approach extends it to continuous text without punctuation as is the case with video transcripts. The "naive context" sentiment extractor (Kleinberg et al, 2018) accounts for valence shifters, which influence the meaning of the sentiment. Negators (e.g., not, doesn't), [de-]amplifiers (e.g., really, hardly), and adversative conjunctions (e.g., but, however) were included.…”

Section: Feature Extractionmentioning

confidence: 99%

Uphill from here: Sentiment patterns in videos from left- and right-wing

Soldner¹,

Ho²,

Makhortykh³

et al. 2019

Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

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News consumption exhibits an increasing shift towards online sources, which bring platforms such as YouTube more into focus. Thus, the distribution of politically loaded news is easier, receives more attention, but also raises the concern of forming isolated ideological communities. Understanding how such news is communicated and received is becoming increasingly important. To expand our understanding in this domain, we apply a linguistic temporal trajectory analysis to analyze sentiment patterns in English-language videos from news channels on YouTube. We examine transcripts from videos distributed through eight channels with pro-left and pro-right political leanings. Using unsupervised clustering, we identify seven different sentiment patterns in the transcripts. We found that the use of two sentiment patterns differed significantly depending on political leaning. Furthermore, we used predictive models to examine how different sentiment patterns relate to video popularity and if they differ depending on the channel's political leaning. No clear relations between sentiment patterns and popularity were found. However, results indicate, that videos from pro-right news channels are more popular and that a negative sentiment further increases that popularity, when sentiments are averaged for each video.

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Identifying the sentiment styles of YouTube’s vloggers

Cited by 12 publications

References 19 publications

The temporal evolution of a far-right forum

The temporal evolution of a far-right forum

Online influence, offline violence: language use on YouTube surrounding the ‘Unite the Right’ rally

Uphill from here: Sentiment patterns in videos from left- and right-wing

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