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On the relation between message sentiment and its virality on social media

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Abstract

We investigate the relation between the sentiment of a message on social media and its virality, defined as the volume and speed of message diffusion. We analyze 4.1 million messages (tweets) obtained from Twitter. Although factors affecting message diffusion on social media have been studied previously, we focus on message sentiment and reveal how the polarity of message sentiment affects its virality. The virality of a message is characterized by the number of message repostings (retweets) and the time elapsed from the original posting of a message to its Nth reposting (N-retweet time). Through extensive analysis using the 4.1 million tweets and their retweets in 1 week, we discover that negative messages are likely to be reposted more rapidly and frequently than positive and neutral messages. Specifically, the reposting volume of negative messages is 20–60% higher than that of positive and neutral messages, and negative messages spread 25% faster than positive and neutral messages when the diffusion volume is quite high. We also perform longitudinal analysis of message diffusion observed over 1 year and find that recurrent diffusion of negative messages is less frequent than that of positive and neutral messages. Moreover, we present a simple message diffusion model that can reproduce the characteristics of message diffusion observed in this paper.

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Notes

  1. We used the Search API in Twitter REST API v1.1 and collected Japanese tweets using the query q=RT, lang=ja.

  2. http://taku910.github.io/mecab/

References

  • Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of Twitter data. In: Proceedings of the workshop on languages in social media (LSM’11), pp 30–38

  • Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the 4th ACM international conference on web search and data mining (WSDM’11), pp 65–74

  • Bakshy E, Rosenn I, Marlow C, Adamic L (2012) The role of social networks in information diffusion. In: Proceedings of the 21st international conference on world wide web (WWW’12), pp 519–528

  • Barbosa L, Feng J (2010) Robust sentiment detection on Twitter from biased and noisy data. In: Proceedings of the 23rd international conference on computational linguistics (COLING’10), pp 36–44

  • Baumeister RF, Bratslavsky E (2001) Bad is stronger than good. Rev Gen Psychol 5(4):323–370

    Article  Google Scholar 

  • Blei D, Ng A, Jordan M (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993l–1022

    MATH  Google Scholar 

  • Budak C, Agrawal D, El Abbadi A (2011) Limiting the spread of misinformation in social networks. In: Proceedings of the 20th international conference on world wide web (WWW’11), pp 665–674

  • Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J (2014) Can cascades be predicted? In: Proceedings of the 23rd international conference on world wide web (WWW’14), pp 925–936

  • Cheng J, Adamic LA, Kleinberg J, Leskovec J (2016) Do cascades recur? In: Proceedings of the 25th international conference on world wide web (WWW’16), pp 671–681

  • De Domenico M, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Sci Rep 3:2980:1–2980:9

    Article  Google Scholar 

  • Dwass M (1960) Some \(k\)-sample rank-order tests. In: Contributions to probability and statistics, Stanford University Press, pp 198–202

  • Ferrara E, Yang Z (2015) Quantifying the effect of sentiment on information diffusion in social media. PeerJ Comput Sci 1:e26

    Article  Google Scholar 

  • Gonçalves P, Araújo M, Benevenuto F, Cha M (2013) Comparing and combining sentiment analysis methods. In: Proceedings of the first ACM conference on online social networks (COSN’13), pp 27–38

  • Gruzd A, Doiron S, Mai P (2011) Is happiness contagious online? A case of Twitter and the 2010 Winter Olympics. In: Proceedings of the 44th Hawaii international conference on system sciences (HICSS’11), pp 1–9

  • Hansen L, Arvidsson A, Nielsen F, Colleoni E, Etter M (2011) Good friends, bad news-affect and virality in Twitter. Future Inf Technol 185:34–43

    Article  Google Scholar 

  • Hong L, Convertino G, Chi EH (2011a) Language matters in Twitter: a large scale study. In: Proceedings of the 5th international AAAI conference on weblogs and social media (ICWSM’11), pp 518–521

  • Hong L, Dan O, Davison B (2011b) Predicting popular messages in Twitter. In: Proceedings of the 20th international conference on world wide web (WWW’11), pp 57–58

  • Karsai M, Kaski K, Barabási AL, Kertész J (2012) Universal features of correlated bursty behaviour. Sci. Rep. 2:397:1–397:7

    Article  Google Scholar 

  • Kempe D, Kleinberg JM, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’03), pp 137–146

  • Kontopoulos E, Berberidis C, Dergiades T, Bassiliades N (2013) Ontology-based sentiment analysis of Twitter posts. Expert Syst Appl 40(10):4065–4074

    Article  Google Scholar 

  • Kupavskii A, Ostroumova L, Umnov A, Usachev S, Serdyukov P, Gusev G, Kustarev A (2012) Prediction of retweet cascade size over time. In: Proceedings of the 21st ACM international conference on information and knowledge management (CIKM’12), pp 2335–2338

  • Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web (WWW’10), pp 591–600

  • Liu B, Cong G, Xu D, Zeng Y (2012) Time constrained influence maximization in social networks. In: Proceedings of the IEEE 12th international conference on data mining (ICDM’12), pp 439–448

  • Naveed N, Gottron T, Kunegis J, Alhadi A (2011) Bad news travel fast: A content-based analysis of interestingness on Twitter. In: Proceedings of the ACM web science conference 2011 (WebSci’11), pp 1–7

  • Rosenthal S, Nakov P, Kiritchenko S, Mohammad SM, Ritter A, Stoyanov V (2015) SemEval-2015 task 10: Sentiment analysis in Twitter. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval’15), pp 451–463

  • Rozin P, Royzman EB (2001) Negativity bias, negativity dominance, and contagion. Personal Soc Psychol Rev 5(4):296–320

    Article  Google Scholar 

  • Steel RGD (1960) A rank sum test for comparing all pairs of treatments. Technometrics 2(2):197–207

    Article  MathSciNet  MATH  Google Scholar 

  • Stieglitz S, Dang-Xuan L (2012) Political communication and influence through microblogging—an empirical analysis of sentiment in Twitter messages and retweet behavior. In: Proceedings of the 45th Hawaii international conference on system science (HICSS’12), pp 3500–3509

  • Stieglitz S, Dang-Xuan L (2013) Emotions and information diffusion in social media–sentiment of microblogs and sharing behavior. J Manag Inf Syst 29(4):217–247

    Article  Google Scholar 

  • Suh B, Hong L, Pirolli P, Chi E (2010) Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In: Proceedings of the 2nd IEEE international conference on social computing (SocialCom’10), pp 177–184

  • Takamura H, Inui T, Okumura M (2005) Extracting semantic orientations of words using spin model. In: Proceedings of the 43rd annual meeting on association for computational linguistics (ACL’05), pp 133–140

  • Takamura H, Inui T, Okumura M (2006) Extracting semantic orientations using spin model. IPSJ J 47(2):627–637 (in Japanese)

    Google Scholar 

  • Taylor SE (1991) Asymmetrical effects of positive and negative events: The mobilization-minimization hypothesis. Psychol Bull 110(1):67–85

    Article  Google Scholar 

  • Tsugawa S, Ohsaki H (2015) Negative messages spread rapidly and widely on social media. In: Proceedings of the 2015 ACM conference on online social networks (COSN’15), pp 151–160

  • Wen S, Jiang J, Xiang Y, Yu S, Zhou W, Jia W (2014) To shut them up or to clarify: restraining the spread of rumors in online social networks. IEEE Trans Parallel Distrib Syst 25(12):3306–3316

    Article  Google Scholar 

  • Yang S, Kolcz A, Schlaikjer A, Gupta P (2014) Large-scale high-precision topic modeling on Twitter. In: Proceedings of the 20th ACM SIGKDD conference on knowledge discovery and data mining (KDD’14), pp 1907–1916

  • Zhao K, Stehlé J, Bianconi G, Barrat A (2011) Social network dynamics of face-to-face interactions. Phys Rev E 83(5):056109

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Dr. Mitsuo Yoshida of Toyohashi University of Technology for his support to the data collection and Hisayuki Mori of Kwansei Gakuin University for helping the analyses. This work was partly supported by the Telecommunications Advancement Foundation.

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Authors and Affiliations

  1. Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Sho Tsugawa

  2. School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

    Hiroyuki Ohsaki

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  1. Sho Tsugawa
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  2. Hiroyuki Ohsaki
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Correspondence to Sho Tsugawa.

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Tsugawa, S., Ohsaki, H. On the relation between message sentiment and its virality on social media. Soc. Netw. Anal. Min. 7, 19 (2017). https://doi.org/10.1007/s13278-017-0439-0

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  • DOI: https://doi.org/10.1007/s13278-017-0439-0

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