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On the influence of emotional valence shifts on the spread of information in social networks

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    In this paper, we present a study on 4.4 million Twitter messages related to 24 systematically chosen real-world events. For each of the 4.4 million tweets, we first extracted sentiment scores based on the eight basic emotions according to Plutchik’s wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the spread of information. We found that in general OSN users tend to conform to the emotional valence of the respective real-world event. However, we also found empirical evidence that prospectively negative real-world events exhibit a significant amount of shifted emotions in the corresponding tweets (i.e. positive messages). To explain this finding, we use the theory of social connection and emotional contagion. To the best of our knowledge, this is the first study that provides empirical evidence for the undoing hypothesis in online social networks (OSNs). The undoing hypothesis postulates that positive emotions serve as an antidote during negative events.

    Original languageEnglish
    Title of host publicationProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
    EditorsJana Diesner, Elena Ferrari, Guandong Xu
    PublisherAssociation for Computing Machinery
    Pages321-324
    Number of pages4
    ISBN (Electronic)9781450349932
    DOIs
    Publication statusPublished - 31 Jul 2017
    Event9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017 - Sydney, Australia
    Duration: 31 Jul 20173 Aug 2017

    Publication series

    NameProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017

    Conference

    Conference9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
    Country/TerritoryAustralia
    CitySydney
    Period31/07/173/08/17

    Keywords

    • Diffusion
    • Sentiment analysis
    • Twitter

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