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Periodicity Detection of Emotional Communities in Microblogging

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AI*IA 2019 – Advances in Artificial Intelligence (AI*IA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11946))

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Abstract

Social media allow users convey emotions, which are often related to real-world events, social relationships or personal experiences. Indeed, emotions can determine the propension of the users to socialize or attend events. Similarly, interactions with people can influence the personality and feelings of the individuals. Therefore, studying emotional content generated by the users can reveal information on the behavior of users or collectives of users. However, such an information is related only to a specific moment when the emotions are sporadic or episodic, therefore they could have little usefulness. On the contrary, it can have greater significance tracing emotions over time and understanding whether they may appear with regularity or whether they are associated to behaviors already observed in past and could recur.

In this paper, we focus on the periodicity with which emotional words appear in the micro-blogs as indication of a collective emotional behavior expressed with regularity. We propose a computational solution that builds a cyberspace based on the emotional content produced by the users and determines communities of users who express with periodicity similar emotional behaviors. We show the viability of the method on the data of the social media platform Twitter and provide a quantitative evaluation and qualitative considerations.

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Notes

  1. 1.

    The corpora was downloaded on January 2018 from the link https://old.datahub.io/dataset/twitter-2012-presidential-election/resource/9bb14d78-9519-459a-9fad-e630e3e9a0a1.

References

  1. Ceci, M., Loglisci, C., Macchia, L.: Ranking sentences for keyphrase extraction: a relational data mining approach. In: Agosti, M., Catarci, T., Esposito, F. (eds.) Pushing the Boundaries of the Digital Libraries Field - 10th Italian Research Conference on Digital Libraries, IRCDL 2014, Padua, Italy, 30–31 January 2014. Procedia Computer Science, vol. 38, pp. 52–59. Elsevier (2014). https://doi.org/10.1016/j.procs.2014.10.011

    Article  Google Scholar 

  2. Chen, X., Sykora, M.D., Jackson, T.W., Elayan, S.: What about mood swings: identifying depression on twitter with temporal measures of emotions. In: Companion Proceedings of the The Web Conference 2018, WWW 2018, International World Wide Web Conferences Steering Committee, pp. 1653–1660 (2018). https://doi.org/10.1145/3184558.3191624

  3. Ekman, P.: Facial expression and emotion. Am. psychol. 48, 384–92 (1993)

    Article  Google Scholar 

  4. Garas, A., Garcia, D., Skowron, M., Schweitzer, F.: Emotional persistence in online chatting communities. Sci. Rep. 2, 402 (2012). https://doi.org/10.1038/srep00402

    Article  Google Scholar 

  5. Golder, S., Macy, M.: Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333(6051), 1878–1881 (2011). https://doi.org/10.1126/science.1202775

    Article  Google Scholar 

  6. Huang, K., Chang, C.: SMCA: a general model for mining asynchronous periodic patterns in temporal databases. IEEE Trans. Knowl. Data Eng. 17(6), 774–785 (2005). https://doi.org/10.1109/TKDE.2005.98

    Article  Google Scholar 

  7. Jin, S., Zafarani, R.: Emotions in social networks: distributions, patterns, and models. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 1907–1916 (2017). https://doi.org/10.1145/3132847.3132932

  8. Kanavos, A., Perikos, I.: Towards detecting emotional communities in twitter. In: 9th IEEE International Conference on Research Challenges in Information Science, RCIS 2015, Athens, Greece, 13–15 May 2015, pp. 524–525 (2015). https://doi.org/10.1109/RCIS.2015.7128919

  9. Keene, J.R., Lang, A.: Dynamic motivated processing of emotional trajectories in public service announcements. Commun. Monogr. 83(4), 468–485 (2016). https://doi.org/10.1080/03637751.2016.1198040

    Article  Google Scholar 

  10. Lai, M., Patti, V., Ruffo, G., Rosso, P.: Stance evolution and twitter interactions in an Italian political debate. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds.) NLDB 2018. LNCS, vol. 10859, pp. 15–27. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91947-8_2

    Chapter  Google Scholar 

  11. Layer, R.M., Skadron, K., Robins, G., Hall, I.M., Quinlan, A.R.: Binary interval search: a scalable algorithm for counting interval intersections. Bioinformatics 29(1), 1–7 (2013). https://doi.org/10.1093/bioinformatics/bts652

    Article  Google Scholar 

  12. Lerman, K., Arora, M., Gallegos, L., Kumaraguru, P., Garcia, D.: Emotions, demographics and sociability in twitter interactions. In: Tenth International Conference on Web and Social Media, Cologne, Germany, vol. 2016, pp. 201–210 (2016)

    Google Scholar 

  13. Li, Z., Ding, B., Han, J., Kays, R.: Swarm: mining relaxed temporal moving object clusters. PVLDB 3(1), 723–734 (2010). https://doi.org/10.14778/1920841.1920934

    Article  Google Scholar 

  14. Loglisci, C., Andresini, G., Impedovo, A., Malerba, D.: Analyzing microblogging posts for tracking collective emotional trajectories. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds.) AI*IA 2018. LNCS (LNAI), vol. 11298, pp. 123–135. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03840-3_10

    Chapter  Google Scholar 

  15. Loglisci, C., Appice, A., Malerba, D.: Collective regression for handling autocorrelation of network data in a transductive setting. J. Intell. Inf. Syst. 46(3), 447–472 (2016). https://doi.org/10.1007/s10844-015-0361-8

    Article  Google Scholar 

  16. Loglisci, C., Ceci, M., Malerba, D.: Discovering evolution chains in dynamic networks. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) NFMCP 2012. LNCS (LNAI), vol. 7765, pp. 185–199. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37382-4_13

    Chapter  Google Scholar 

  17. Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). https://doi.org/10.1145/219717.219748

    Article  Google Scholar 

  18. Mohammad, S., Bravo-Marquez, F., Salameh, M., Kiritchenko, S.: Semeval-2018 task 1: affect in tweets. In: Proceedings of The 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2018, New Orleans, Louisiana, USA, 5–6 June 2018, pp. 1–17 (2018)

    Google Scholar 

  19. Nissim, M., Patti, V.: Semantic Aspects in Sentiment Analysis. Morgan Kaufmann, Burlington (2016). https://doi.org/10.1016/B978-0-12-804412-4.00003-6

    Book  Google Scholar 

  20. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7

    Article  MATH  Google Scholar 

  21. Sano, Y., Takayasu, H., Havlin, S., Takayasu, M.: Identifying long-term periodic cycles and memories of collective emotion in online social media. PLoS ONE 14(3), e0213843 (2019). https://doi.org/10.1371/journal.pone.0213843

    Article  Google Scholar 

  22. Strapparava, C., Valitutti, A.: Wordnet affect: an affective extension of wordnet. In: Proceedings of the Fourth International Conference on Language Resources and Evaluation, LREC 2004, Lisbon, Portugal, 26–28 May 2004. European Language Resources Association (2004)

    Google Scholar 

  23. Zhou, Q., Zhang, C.: Emotion evolutions of sub-topics about popular events on microblogs. Electron. Lib. 35(4), 770–782 (2017). https://doi.org/10.1108/EL-09-2016-0184

    Article  Google Scholar 

  24. Zhu, C., Zhu, H., Ge, Y., Chen, E., Liu, Q.: Tracking the evolution of social emotions: a time-aware topic modeling perspective. In: 2014 IEEE International Conference on Data Mining, ICDM 2014, Shenzhen, China, 14–17 December 2014, pp. 697–706 (2014). https://doi.org/10.1109/ICDM.2014.121

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Acknowledgments

This work fulfills the objectives the project “Computer-mediated collaboration in creative projects” (8GPS5R0) collocated in “Intervento cofinanziato dal Fondo di Sviluppo e Coesione 2007-2013 – APQ Ricerca Regione Puglia - Programma regionale a sostegno della specializzazione intelligente e della sostenibilita’ sociale ed ambientale - FutureInResearch”.

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Authors and Affiliations

  1. Department of Computer Science, Universita’ degli Studi di Bari “Aldo Moro”, Bari, Italy

    Corrado Loglisci & Donato Malerba

  2. CINI - Consorzio Interuniversitario Nazionale per l’Informatica, Rome, Italy

    Corrado Loglisci & Donato Malerba

Authors
  1. Corrado Loglisci
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  2. Donato Malerba
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Corresponding author

Correspondence to Corrado Loglisci .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Mario Alviano

  2. University of Calabria, Rende, Italy

    Gianluigi Greco

  3. University of Calabria, Rende, Italy

    Francesco Scarcello

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Loglisci, C., Malerba, D. (2019). Periodicity Detection of Emotional Communities in Microblogging. In: Alviano, M., Greco, G., Scarcello, F. (eds) AI*IA 2019 – Advances in Artificial Intelligence. AI*IA 2019. Lecture Notes in Computer Science(), vol 11946. Springer, Cham. https://doi.org/10.1007/978-3-030-35166-3_39

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  • DOI: https://doi.org/10.1007/978-3-030-35166-3_39

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