Political polarization and popularity in online participatory media

García, David; Mendez, Fernando; Serdült, Uwe; Schweitzer, Frank

doi:10.1145/2389661.2389665

Cited by 41 publications

(38 citation statements)

References 31 publications

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“…SentiStrength provides two scales of positive, from 1 to 5, and negative sentiment, from −1 to −5. We classify a post as positive if its positive score is 3 or higher, and negative if its negative score is −3 or lower, following the methods of previous research [17,22,59]. …”

Section: Methodsmentioning

confidence: 99%

The dynamics of emotions in online interaction

et al. 2016

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We study the changes in emotional states induced by reading and participating in online discussions, empirically testing a computational model of online emotional interaction. Using principles of dynamical systems, we quantify changes in valence and arousal through subjective reports, as recorded in three independent studies including 207 participants (110 female). In the context of online discussions, the dynamics of valence and arousal is composed of two forces: an internal relaxation towards baseline values independent of the emotional charge of the discussion and a driving force of emotional states that depends on the content of the discussion. The dynamics of valence show the existence of positive and negative tendencies, while arousal increases when reading emotional content regardless of its polarity. The tendency of participants to take part in the discussion increases with positive arousal. When participating in an online discussion, the content of participants' expression depends on their valence, and their arousal significantly decreases afterwards as a regulation mechanism. We illustrate how these results allow the design of agent-based models to reproduce and analyse emotions in online communities. Our work empirically validates the microdynamics of a model of online collective emotions, bridging online data analysis with research in the laboratory.

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Section: Methodsmentioning

confidence: 99%

The dynamics of emotions in online interaction

et al. 2016

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“…2 To each tweet in our dataset, quantifying positive sentiment P 2 ½þ1; þ5] and negative sentiment N 2 ½À1; À5], consistently with the Positive and Negative Affect Schedule (PANAS) [42]. SentiStrength has been shown to perform very closely to human raters in validity tests [41] and has been applied to measure emotions in product reviews [43], online chatrooms [44], Yahoo answers [45], Youtube comments [46], and social media discussions [47]. In addition, SentiStrength allows our approach to be applied in the future to other languages, like Spanish [30,48], and to include contextual factors [49], like sarcasm [50].…”

Section: Emotion Analysismentioning

confidence: 72%

Language, demographics, emotions, and the structure of online social networks

Lerman¹,

Marin²,

Arora

et al. 2017

J Comput Soc Sc

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Social networks affect individuals' economic opportunities and wellbeing. However, few of the factors thought to shape networks-culture, language, education, and income-were empirically validated at scale. To fill this gap, we collected a large number of social media posts from a major US metropolitan area. By associating these posts with US Census tracts through their locations, we linked socioeconomic indicators to group-level signals extracted from social media, including emotions, language, and online social ties. Our analysis shows that tracts with higher education levels have weaker social ties, but this effect is attenuated for tracts with high ratio of Hispanic residents. Negative emotions are associated with

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“…In addition to the topics expressed in the posts, we also study collective emotions through the sentiment analysis according to the method introduced in [20]. We processed the text of each post using SentiWordNet [21], which is a lexical resource for the extraction of emotional content from a short text.…”

Section: B Collective Emotion In Tobacco Communitiesmentioning

confidence: 99%

An Integrated Approach of Sensing Tobacco-Oriented Activities in Online Participatory Media

Liang

Zhou

Zeng

et al. 2016

IEEE Systems Journal

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With the embracing of mobile Internet of Things, participatory media is becoming a new battlefield for tobacco wars. However, how to automatically collect and analyze the large-scale user-generated data that are relevant to tobacco in participatory media is still unexplored. In this paper, we propose an integrated approach of sensing collective activities in tobacco-related participatory media. Meanwhile, we compare the temporal patterns, topological patterns, and collective emotion patterns among protobacco, antitobacco, and quit-tobacco groups. Based on the proposed framework, a prototype was implemented to collect large-scale tobacco-related data sets from Facebook. This demonstrates that the proposed framework is feasible and reasonable. Our preliminary findings reveal that, first, tobaccorelated content grows exponentially. In particular, the growth of the protobacco group follows the exponential law, whereas there is linear growth for the tobacco control group. Second, the connection between the tobacco control community and the tobacco cessation community is tight, whereas the nodes in the protobacco community are connected sparsely. Finally, the collective emotion in tobacco communities is negative.

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Political polarization and popularity in online participatory media

Cited by 41 publications

References 31 publications

The dynamics of emotions in online interaction

The dynamics of emotions in online interaction

Language, demographics, emotions, and the structure of online social networks

An Integrated Approach of Sensing Tobacco-Oriented Activities in Online Participatory Media

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