Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks

Robert Hillmann, Matthias Trier

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    Communication in online social networks has been analyzed for some time regarding the expression of sentiments. So far, very little is known about the relationship between sentiments and network emergence, dissemination patterns
    and possible differences between positive and negative sentiments. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social networks exhibits a strong tendency towards reciprocity, followed by the dominance of
    hierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only play a secondary role in network emergence and do not express differences regarding the emergence of network patterns.
    Communication in online social networks has been analyzed for some time regarding the expression of sentiments. So far, very little is known about the relationship between sentiments and network emergence, dissemination patterns
    and possible differences between positive and negative sentiments. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social networks exhibits a strong tendency towards reciprocity, followed by the dominance of
    hierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only play a secondary role in network emergence and do not express differences regarding the emergence of network patterns.
    LanguageEnglish
    Title of host publicationProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
    Number of pages6
    PublisherIEEE
    Date2012
    Pages510-515
    ISBN (Print)9780769547992
    StatePublished - 2012
    EventThe 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining: ASONAM 2012 - Kadir Has University, Istanbul, Turkey
    Duration: 26 Aug 201229 Aug 2012
    http://www.asonam2012.etu.edu.tr/

    Conference

    ConferenceThe 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
    LocationKadir Has University
    CountryTurkey
    CityIstanbul
    Period26/08/201229/08/2012
    Other<br/><br/><br/>
    Internet address

    Bibliographical note

    CBS Library does not have access to the material

    Keywords

      Cite this

      Hillmann, R., & Trier, M. (2012). Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks. In Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 510-515). IEEE.
      Hillmann, Robert ; Trier, Matthias. / Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks. Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2012. pp. 510-515
      @inproceedings{994bb0b3ceb44d4281cb26010c6f7632,
      title = "Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks",
      abstract = "Communication in online social networks has been analyzed for some time regarding the expression of sentiments. So far, very little is known about the relationship between sentiments and network emergence, dissemination patternsand possible differences between positive and negative sentiments. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social networks exhibits a strong tendency towards reciprocity, followed by the dominance ofhierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only play a secondary role in network emergence and do not express differences regarding the emergence of network patterns.",
      keywords = "Social Network Analysis, Dynamic Network Motif Analysis, Sentiment Dissemination, Networking Effects, Triads",
      author = "Robert Hillmann and Matthias Trier",
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      year = "2012",
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      isbn = "9780769547992",
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      Hillmann, R & Trier, M 2012, Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks. in Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, pp. 510-515, Istanbul, Turkey, 26/08/2012.

      Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks. / Hillmann, Robert; Trier, Matthias.

      Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2012. p. 510-515.

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

      TY - GEN

      T1 - Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks

      AU - Hillmann,Robert

      AU - Trier,Matthias

      N1 - CBS Library does not have access to the material

      PY - 2012

      Y1 - 2012

      N2 - Communication in online social networks has been analyzed for some time regarding the expression of sentiments. So far, very little is known about the relationship between sentiments and network emergence, dissemination patternsand possible differences between positive and negative sentiments. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social networks exhibits a strong tendency towards reciprocity, followed by the dominance ofhierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only play a secondary role in network emergence and do not express differences regarding the emergence of network patterns.

      AB - Communication in online social networks has been analyzed for some time regarding the expression of sentiments. So far, very little is known about the relationship between sentiments and network emergence, dissemination patternsand possible differences between positive and negative sentiments. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social networks exhibits a strong tendency towards reciprocity, followed by the dominance ofhierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only play a secondary role in network emergence and do not express differences regarding the emergence of network patterns.

      KW - Social Network Analysis

      KW - Dynamic Network Motif Analysis

      KW - Sentiment Dissemination

      KW - Networking Effects

      KW - Triads

      M3 - Article in proceedings

      SN - 9780769547992

      SP - 510

      EP - 515

      BT - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

      PB - IEEE

      ER -

      Hillmann R, Trier M. Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks. In Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE. 2012. p. 510-515.