Hashtags and followers

详细信息    查看全文

• 作者：Eva García Martín ; Niklas Lavesson ; Mina Doroud
• 关键词：Experimental study ; Correlational analysis ; Hashtags ; Followers
• 刊名：Social Network Analysis and Mining
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：6
• 期：1
• 全文大小：1,623 KB
• 参考文献：Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finance 23(4):589–609CrossRef
  Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512MathSciNet CrossRef MATH
  Bifet A, Frank E (2010) Sentiment knowledge discovery in twitter streaming data. In: Discovery science. Springer, Berlin, pp 1–15
  Bray P (2012) When is my tweet’s prime of life? A brief statistical interlude. http://​moz.​com/​blog/​when-is-my-tweets-prime-of-life
  Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring user influence in twitter: the million follower fallacy. In: 4th international AAAI conference on weblogs and social media (ICWSM), vol 14, p 8
  Cochran WG (2007) Sampling techniques. Wiley, New York
  Diakopoulos NA, Shamma DA (2010) Characterizing debate performance via aggregated twitter sentiment. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 1195–1198
  Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 57–66
  Given LM (2008) Qualitative research methods, vol 2. Sage, Chennai
  Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N project report, Stanford, pp 1–12
  Huberman B, Romero D, Wu F (2008) Social networks that matter: Twitter under the microscope. Available at SSRN 1313405
  Hutto C, Yardi S, Gilbert E (2013) A longitudinal study of follow predictors on twitter. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 821–830
  Jeong H, Néda Z, Barabási AL (2003) Measuring preferential attachment in evolving networks. EPL Europhys Lett 61(4):567CrossRef
  Jungselius B, Hilman T, Weilenmann A (2014) Fishing for followers: using hashtags as like bait in social media. Selected papers of internet
  Katz E, Lazarsfeld PF (1955) Personal influence. In: The part played by people in the flow of mass communications. Transaction Publishers, Piscataway
  Kivran-Swaine F, Naaman M (2011) Network properties and social sharing of emotions in social awareness streams. In: Proceedings of the ACM 2011 conference on computer supported cooperative work. ACM, New York, pp 379–382
  Kivran-Swaine F, Govindan P, Naaman M (2011) The impact of network structure on breaking ties in online social networks: unfollowing on twitter. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 1101–1104
  Kolmogorov AN (1933) Sulla determinazione empirica di una legge di distribuzione. Giornale dellIstituto Italiano degli Attuari 4(1):83–91MATH
  Kong S, Mei Q, Feng L, Zhao Z (2014) Real-time predicting bursting hashtags on twitter. In: Web-age information management. Springer, Berlin, pp 268–271
  Kumar R, Novak J, Tomkins A (2010) Structure and evolution of online social networks. In: Link mining: models, algorithms, and applications. Springer, Berlin, pp 337–357
  Lang J, Wu SF (2011) Anti-preferential attachment: if i follow you, will you follow me? In: Privacy, security, risk and trust (passat). In: 2011 IEEE third international conference on social computing (socialcom). IEEE, New York, pp 339–346
  Lardinois F (2009) The short lifespan of a tweet: retweets only happen within the first hour. Read Write Web (September 2009). Accessed 20 Febr 2012. http://​wwwreadwritewebc​om/​archives/​the-short-lifespan-of-a-tweet-retweets-only-happenphp
  Makice K (2009) Twitter API: up and running. Oreilly & Associates Incorporated, CA
  Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18(1):50–60MathSciNet CrossRef MATH
  Maruf HA, Mahmud J, Ali ME (2014) Can hashtags bear the testimony of personality? Predicting personality from hashtag use
  Mathioudakis M, Koudas N (2010) Twittermonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data. ACM, New York, pp 1155–1158
  Mislove AE (2009) Online social networks: measurement, analysis, and applications to distributed information systems. ProQuest, Ann Arbor
  Moore DS, McCabe GP (2011) Introduction to the practice of statistics. AMC 10:12
  Newman ME (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64(2):025102
  Nia R, Erlandsson F, Johnson H, Wu SF (2013) Leveraging social interactions to suggest friends. In: 2013 IEEE 33rd international conference on distributed computing systems workshops (ICDCSW). IEEE, New York, pp 386–391
  Otsuka E, Wallace SA, Chiu D (2014) Design and evaluation of a twitter hashtag recommendation system. In: Proceedings of the 18th international database engineering & applications symposium. ACM, New York, IDEAS ’14, pp 330–333
  Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: LREC
  Peirce CS, Hartshorne C, Weiss P (1935) Collected papers of charles sanders peirce, vol 5. Harvard University Press, Massachusetts
  Qiu L, Rui H, Whinston A (2011) A twitter-based prediction market: social network approach. In: ICIS 2011 proceedings
  Quercia D, Ellis J, Capra L, Crowcroft J (2011) In the mood for being influential on twitter. In: Privacy, security, risk and trust (PASSAT) and 2011 IEEE third international conference on social computing (SocialCom). IEEE, New York, pp 307–314
  Ritterman J, Osborne M, Klein E (2009) Using prediction markets and twitter to predict a swine flu pandemic. In: 1st international workshop on mining social media
  Rogers EM (2010) Diffusion of innovations. Simon and Schuster, New York
  Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World Wide Web. ACM, New York, pp 851–860
  Shadish WR, Cook TD, Campbell DT (2002) Experimental and quasi-experimental designs for generalized causal inference. Wadsworth Cengage learning, Belmont
  She J, Chen L (2014) Tomoha: topic model-based hashtag recommendation on twitter. In: Proceedings of the companion publication of the 23rd international conference on World wide web companion. International World Wide Web Conferences Steering Committee, Quebec, pp 371–372
  Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. CRC Press, Boca Raton
  Starnes DS, Yates D, Moore D (2010) The practice of statistics. Macmillan, London
  Suh B, Hong L, Pirolli P, Chi EH (2010) Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In: 2010 IEEE second international conference on social computing (SocialCom). IEEE, New York, pp 177–184
  Terdiman D (2012) Report: Twitter hits half a billion tweets a day. http://​news.​cnet.​com/​8301-1023_​3-57541566-93/​report-twitter-hits-half-a-billion-tweets-a-day/​
  Thelwall M, Buckley K, Paltoglou G (2011) Sentiment in twitter events. J Am Soc Inf Sci Technol 62(2):406–418CrossRef
  Wang T, Wang KC, Erlandsson F, Wu SF, Faris R (2013) The influence of feedback with different opinions on continued user participation in online newsgroups. In: 2013 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, New York, pp 388–395
  Wang X, Wei F, Liu X, Zhou M, Zhang M (2011) Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, New York, pp 1031–1040
  Wang Y, Qu J, Liu J, Chen J, Huang Y (2014) What to tag your microblog: hashtag recommendation based on topic analysis and collaborative filtering. In: Web technologies and applications. Springer, Berlin, pp 610–618
  Yu J, Shen Y (2014) Evolutionary personalized hashtag recommendation. In: Web-age information management. Springer, Berlin, pp 34–37
  
• 作者单位：Eva García Martín (1)
  Niklas Lavesson (1)
  Mina Doroud (2)
  
  1. Blekinge Institute of Technology, Karlskrona, Sweden
  2. Twitter Inc., San Francisco, USA
  
• 刊物类别：Computer Science
• 刊物主题：Sociology
  Data Mining and Knowledge Discovery
  Theoretical Ecology
  Game Theory
  
• 出版者：Springer Wien
• ISSN：1869-5469

文摘

We have conducted an analysis of data from 502,891 Twitter users and focused on investigating the potential correlation between hashtags and the increase of followers to determine whether the addition of hashtags to tweets produces new followers. We have designed an experiment with two groups of users: one tweeting with random hashtags and one tweeting without hashtags. The results showed that there is a correlation between hashtags and followers: on average, users tweeting with hashtags increased their followers by 2.88, while users tweeting without hashtags increased 0.88 followers. We present a simple, reproducible approach to extract and analyze Twitter user data for this and similar purposes.