Quantifying the Effect of Sentiment on Topic Evolution in Chinese Microblog

Fu, Peng; Lin, Zheng; Lin, Hailun; Yuan, Fengcheng; Wang, Weiping; Meng, Dan

doi:10.1007/978-3-319-45814-4_43

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…While this could be considered counter-intuitive given the extant literature suggesting a negativity bias in transmission experiments and cultural artefacts (Bebbington et al, 2016;Brand et al, 2019;Fessler et al, 2014;Morin and Acerbi, 2017), tales of the morally virtuous have also been found to be culturally successful in mythology (Bierlein, 2010). Further, as previously mentioned, analyses of social media show positively valenced content to be more successful than negatively valenced content (Ferrara and Yang, 2015a;Ferrara and Yang, 2015b;Fu et al, 2016), while children display a bias towards receiving and transmitting information portraying their in-group as pro-social (Dunham et al, 2011;Over et al, 2017) and adults display a preference for transmitting positive vignettes over negative ones (van Leeuwen et al, 2018). We therefore suggest that a valence-based transmission bias is dependent on a number of contextual factors that we discuss further in the general discussion.…”

Section: Methodsmentioning

confidence: 84%

“…Experimental research has found a preference for choosing to transmit positively valenced vignettes over negatively valenced equivalents (van Leeuwen et al, 2018). Studies using "real world" data have found an advantage for positive content, with positively valenced messages being more frequently and more widely shared on social media than negative content (Ferrara and Yang, 2015a;Ferrara and Yang, 2015b;Fu et al, 2016) and urban legends featuring amusing content being more common than those featuring other, negative emotions . In addition, research has shown that children seek out and transmit content which supports a positive, pro-social, evaluation of their in-group over other types of information, including negative information about their outgroup (Over et al, 2017).…”

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confidence: 99%

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Social transmission favours the ‘morally good’ over the ‘merely arousing’

et al. 2019

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Moral stories are pervasive in human culture, forming the basis of religious texts, folklore, and newspaper articles. We used a linear transmission chain procedure to test three competing hypotheses: (1) that moral content in general is preferentially transmitted between individuals compared to non-moral content; (2) that negativity bias leads specifically to morally bad content being preferentially transmitted; and (3) that a bias towards pro-social information leads specifically to morally good content being preferentially transmitted. While we found no support for a bias for moral content in general, we did find that morally good content was transmitted with greater fidelity than neutral or morally bad content, with ratings of morally good content but not morally bad content predicting transmission. Moral content, therefore, appears to be particularly culturally potent when it describes the 'virtuous' rather than the 'sinful'. A second study repeated the first but also tested the influence of physiological arousal on transmission by measuring the electrodermal activity of participants. This study also found that morally good content was transmitted with greater fidelity than neutral or morally bad content and that physiological arousal had a negative effect on transmission with more arousing material being less faithfully transmitted. These results suggest that the communication of content relating to moral virtue might serve to avoid negative impression formation and promote social bonding, and that this might partially explain the ubiquity of moral content in human culture.

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Section: Methodsmentioning

confidence: 84%

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confidence: 99%

Social transmission favours the ‘morally good’ over the ‘merely arousing’

et al. 2019

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“…However, these works focused on learning candidate refinements of opinion concepts polarity without considering the context dimension associated with them. A variant of this problem is the use of polarity adaptation strategy in the field of social media and microblogs (Alahmadi and Zeng, 2015;Wang et al, 2014;Keshavarz and Abadeh, 2017;Hu et al, 2016;Fu et al, 2016;Gong et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text

Dragoni

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes the NeuroSent system that participated in SemEval 2018 Task 1. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.

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NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model

Dragoni¹

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes the NeuroSent system that participated in SemEval 2018 Task 3. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.

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Quantifying the Effect of Sentiment on Topic Evolution in Chinese Microblog

Cited by 3 publications

References 24 publications

Social transmission favours the ‘morally good’ over the ‘merely arousing’

Social transmission favours the ‘morally good’ over the ‘merely arousing’

NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text

NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model

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