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Abstract

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches, and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories.

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Notes

  1. 1.

    https://goo.gl/Jo1h9U.

  2. 2.

    TweetObject.https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object.

  3. 3.

    UserObject.https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/user-object.

  4. 4.

    How do Neural networks mimic the human brain? https://www.marshall.usc.edu/blog/how-do-neural-networks-mimic-human-brain.

  5. 5.

    https://www.clarifai.com.

  6. 6.

    http://www.pewresearch.org/fact-tank/2016/11/09/why-2016-election-polls-missed-their-mark/.

  7. 7.

    https://goo.gl/mFtzvb.

  8. 8.

    https://goo.gl/AJVpKf.

  9. 9.

    https://goo.gl/sh7WNr.

  10. 10.

    https://goo.gl/iCqzk3.

  11. 11.

    https://goo.gl/i2Ztm6.

  12. 12.

    https://goo.gl/dFCGL9.

  13. 13.

    https://goo.gl/2EhSma.

  14. 14.

    http://blog.knoesis.org/2018/04/debate-on-social-media-for-gun-policy.html.

  15. 15.

    http://time.com/5180006/gun-control-support-has-surged-to-its-highest-level-in-25-years/.

  16. 16.

    https://goo.gl/kgbqWC.

  17. 17.

    https://goo.gl/LMFu3B.

  18. 18.

    These tweets were modified before we share them in this chapter.

  19. 19.

    http://www.wcci2016.org/document/tutorials/ijcnn8.pdf.

  20. 20.

    https://www.cs.cmu.edu/~ark/GeoText/README.txt.

  21. 21.

    http://www.cs.utexas.edu/~roller/research/kd/corpus/README.txt.

  22. 22.

    http://www.pewresearch.org/fact-tank/2018/01/05/americans-support-marijuana-legalization/ft_18-01-05_marijuana_line_update/.

  23. 23.

    http://wiki.knoesis.org/index.php/EDrugTrends.

  24. 24.

    http://linkedgeodata.org/About.

  25. 25.

    http://wiki.knoesis.org/index.php/Context-Aware_Harassment_Detection_on_Social_Media.

  26. 26.

    http://www.pewinternet.org/2014/10/22/online-harassment/.

  27. 27.

    http://cyberbullying.us/facts.

  28. 28.

    https://wiki.openstreetmap.org/wiki/API_v0.6.

  29. 29.

    https://www.socialmediatoday.com/special-columns/adhutchinson/2015-09-09/big-brand-theory-loreal-stays-connected-their-audience.

References

  1. Purohit, H., Sheth, A.: Twitris v3: from citizen sensing to analysis, coordination and action. In: ICWSM (2013)

    Google Scholar 

  2. Davis, C.A., Ciampaglia, G.L., Aiello, L.M., Chung, K., Conover, M.D., Ferrara, E., Flammini, A., Fox, G.C., Gao, X., Gonçalves, B., Grabowicz, P.A., Hong, K., Hui, P.-M., Mccaulay, S., Mckelvey, K., Meiss, M.R., Patil, S., Kankanamalage, C.P., Pentchev, V., Qiu, J., Ratkiewicz, J., Rudnick, A., Serrette, B., Shiralkar, P., Varol, O., Weng, L., Wu, T.-L., Younge, A.J., Menczer F.: OSoMe: the IUNI observatory on social media. PeerJ Comput. Sci. (2016)

    Google Scholar 

  3. Sheth, A., Purohit, H., Smith, G.A., Brunn, J., Jadhav, A., Kapanipathi, P., Lu, C., Wang, W.: Twitris: a system for collective social intelligence. In: Encyclopedia of Social Network Analysis and Mining (2018)

    Google Scholar 

  4. Penuel, K.B., Statler, M.: Encyclopedia of Disaster Relief. Sage Publications, Thousand Oaks (2011)

    Book  Google Scholar 

  5. Malilay, J., Heumann, M., Perrotta, D., Wolkin, A.F., Schnall, A.H., Podgornik, M.N., Cruz, M.A., Horney, J.A., Zane, D., Roisman, R., Greenspan, J.R., Thoroughman, D., Anderson, H.A., Wells, E.V., Simms E.F.: The role of applied epidemiology methods in the disaster management cycle. Am. J. Public Health 104(10), 2092–2102 (2014)

    Article  Google Scholar 

  6. Wang, W., Chen, L., Thirunarayan, K., Sheth, A.P.: Harnessing twitter ‘Big Data’ for automatic emotion identification. In: IEEE International Conference on Social Computing (SocialCom) (2012)

    Google Scholar 

  7. Lamy, F.R., Daniulaityte, R., Nahhas, R.W., Barratt, M.J., Smith, A.G., Sheth, A., Martins, S.S., Boyer, E.W., Carlson, R.G.: Increases in synthetic cannabinoids-related harms: results from a longitudinal web-based content analysis. Int. J. Drug Policy (2017)

    Google Scholar 

  8. Sheth, A., Kapanipathi, P.: Semantic filtering for social data. IEEE Internet Comput. (2016)

    Google Scholar 

  9. Kapanipathi, P., Orlandi, F., Sheth, A., Passant A.: Personalized filtering of the twitter stream. In: SPIM Workshop at ISWC 2011 (2011)

    Google Scholar 

  10. Kapanipathi, P., Jain, P., Venkataramani, C., Sheth, A.: User interests identification on twitter using a hierarchical knowledge base. In: European Semantic Web Conference (2014)

    Google Scholar 

  11. Cameron, D., Smith, G.A., Daniulaityte, R., Sheth, A.P., Dave, D., Chen, L., Anand, G., Carlson, R., Watkins, K.Z., Falck, R.: PREDOSE: a semantic web platform for drug abuse epidemiology using social media. J. Biomed. Inform. 46, 985–997 (2013)

    Article  Google Scholar 

  12. Saif, H.: Semantic Sentiment Analysis in Social Streams. IOS Press, Amsterdam (2017)

    Google Scholar 

  13. Wijeratne, S., Sheth, A., Bhatt, S., Balasuriya, L., Al-Olimat, H.S., Gaur, M., Yazdavar, A.H., Thirunarayan, K.: Feature engineering for twitter-based applications. In: Feature Engineering for Machine Learning and Data Analytics, p. 35 (2017)

    Google Scholar 

  14. Gimpel, K., Schneider, N., O ’connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., Smith, N.A.: Part-of-speech tagging for twitter: annotation, features, and experiments. In: Proceedings of ACL (2011)

    Google Scholar 

  15. Wagner, C., Asur, S., Hailpern, J.: Religious politicians and creative photographers: automatic user categorization in twitter. In: SocialCom (2013)

    Google Scholar 

  16. Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: 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 (2011)

    Google Scholar 

  17. Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 241–249. ACM, New York (2010)

    Google Scholar 

  18. Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: the good the bad and the omg! In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 11), pp. 538–541 (2011)

    Google Scholar 

  19. Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th International Conference on World Wide Web. (2011)

    Google Scholar 

  20. Morstatter, F., Pfeffer, J., Liu, H., Carley, K.M.: Is the sample good enough? Comparing data from twitter’s streaming API with twitter’s firehose. In: ICWSM, pp. 400–408 (2013)

    Google Scholar 

  21. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical Report (2009)

    Google Scholar 

  22. Agarwal, A., Xie, B., Vovsha, I.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Language in Social Media (LSM 2011), pp. 30–38 (2011)

    Google Scholar 

  23. Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In: IEEE International Conference on Social Computing Social Computing (SocialCom) (2010)

    Google Scholar 

  24. Naveed, N., Gottron, T., Kunegis, J., Alhadi, A.C.: Bad news travel fast : a content-based analysis of interestingness on twitter. In: Proceedings of the 3rd International Web Science Conference. ACM, New York (2011)

    Google Scholar 

  25. Thomas, K., Grier, C., Paxson, V.: Suspended accounts in retrospect: an analysis of twitter spam. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement (2011)

    Google Scholar 

  26. Liu, K.-L., Li, W.-J., Guo, M.: Emoticon smoothed language models for twitter sentiment analysis. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)

    Google Scholar 

  27. Zhai, C., Lafferty, J., Lafferty, J., Zhai, C.: A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inform. Syst. 22(2), 179–214 (2004)

    Article  Google Scholar 

  28. Boia, M., Faltings, B.: A :) is worth a thousand words: how people attach sentiment to emoticons and words in tweets. In: SocialCom (2013)

    Google Scholar 

  29. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol. 10 (2010)

    Google Scholar 

  30. Kelly, R., Watts, L.: Characterising the inventive appropriation of emoji as relationally meaningful in mediated close personal relationships. In: Experiences of Technology Appropriation: Unanticipated Users, Usage, Circumstances, and Design (2015)

    Google Scholar 

  31. Novak, P.K., Smailović, J., Sluban, B., Mozetič, I.: Sentiment of emojis. PLOS One (2015)

    Google Scholar 

  32. Miller, H., Thebault-Spieker, J., Chang, S., Johnson, I., Terveen, L., Hecht, B.: ‘Blissfully happy’ or ‘ready to fight’: varying interpretations of emoji. In: International AAAI Conference on Web and Social Media, ICWSM, pp. 259–268 (2016)

    Google Scholar 

  33. Wijeratne, S., Balasuriya, L., Sheth, A., Doran, D.: EmojiNet: an open service and API for emoji sense discovery. In: ICWSM (2017)

    Google Scholar 

  34. Varol, O., Ferrara, E., Menczer, F., Flammini, A.: Early detection of promoted campaigns on social media. EPJ Data Sci. 6(1), 13 (2017)

    Article  Google Scholar 

  35. Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Networks 179, 215–239 (1978)

    Article  Google Scholar 

  36. Freeman, L.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)

    Article  Google Scholar 

  37. Bonacich, P.: Power and centrality : a family of measures. Am. J. Sociol. 92(5), 1170–1182 (1987)

    Article  Google Scholar 

  38. Lawyer, G.: Understanding the influence of all nodes in a network. Nat. Sci. Rep. (2015)

    Google Scholar 

  39. Pennacchiotti, M., Popescu, A.-M.: Democrats, republicans and starbucks afficionados: user classification in twitter. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2011)

    Google Scholar 

  40. Irfan, R., King, C.K., Grages, D., Ewen, S., Khan, S.U., Madani, S.A., Kolodziej, J., Wang, L., Chen, D., Rayes, A., Tziritas, N., Xu, C.-Z., Zomaya, A.Y., Alzahrani, A.S., Li, H.X.: A survey on text mining in social networks. Knowl. Eng. Rev. 000, 1–24 (2004)

    Google Scholar 

  41. Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Chek, D., Ngo, L.: Text mining for market prediction: a systematic review. Expert Syst. Appl. 41, 7653–7670 (2014)

    Article  Google Scholar 

  42. Franch, F.: (Wisdom of the crowds) : 2010 UK election prediction with social media. J. Inform. Technol. Polit. 10(1), 57–71 (2013)

    Article  Google Scholar 

  43. Bravo-Marquez, F., Gayo-Avello, D., Mendoza, M., Poblete, B.: Opinion dynamics of elections in twitter. In: Eighth Latin American Web Congress (2012)

    Google Scholar 

  44. Hong, L., Dan, O., Davison, B.: Predicting popular messages in twitter. In: WWW (2011)

    Google Scholar 

  45. Sokolova, M., Huang, K., Matwin, S., Ramisch, J., Sazonova, V., Black, R., Orwa, C., Ochieng, S., Sambuli, N.: Topic modelling and event identification from twitter textual data (2016). ArXiv preprint

    Google Scholar 

  46. Dumais, S.T.: Latent semantic analysis. Annu. Rev. Inform. Sci. Technol. 3(11), 4356 (2008)

    Google Scholar 

  47. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Proces. Syst. (2013)

    Google Scholar 

  48. Wijeratne, S., Balasuriya, L., Doran, D., Sheth, A., Org, A.: Word embeddings to enhance twitter gang member profile identification. In: IJCAI Workshop on Semantic Machine Learning (2016)

    Google Scholar 

  49. Balasuriya, L., Wijeratne, S., Doran, D., Sheth, A.: Finding street gang members on twitter. In: ASONAM (2016)

    Google Scholar 

  50. Sakaki, S., Miura, Y., Ma, X., Hattori, K., Ohkuma, T.: Twitter user gender inference using combined analysis of text and image processing. In: Proceedings of the 25th International Conference on Computational Linguistics, pp. 54–61 (2014)

    Google Scholar 

  51. Bontcheva, K., Derczynski, L., Funk, A., Greenwood, M.A., Maynard, D., Aswani, N.: TwitIE : an open-source information extraction pipeline for microblog text. In: Proceedings of Recent Advances in Natural Language Processing, pp. 83–90 (2013)

    Google Scholar 

  52. Mitra, T., Gilbert, E.: CREDBANK: a large-scale social media corpus with associated credibility annotations. In: ICWSM (2016)

    Google Scholar 

  53. De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: ICWSM (2013)

    Google Scholar 

  54. Lewenberg, Y., Bachrach, Y., Volkova, S.: Using emotions to predict user interest areas in online social networks. In: Data Science and Advanced Analytics (DSAA) (2015)

    Google Scholar 

  55. Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 U.S. Presidential election cycle. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp. 115–120 (2012)

    Google Scholar 

  56. Ebrahimi, M., Yazdavar, A.H., Sheth, A.: On the challenges of sentiment analysis for dynamic events. IEEE Intell. Syst. (2017)

    Google Scholar 

  57. Vieweg, S., Hughes, A.L., Starbird, K., Palen, L.: Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: CHI - Crisis Informatics (2010)

    Google Scholar 

  58. Chen, L., Wang, W., Sheth, A.P.: Are twitter users equal in predicting elections? A study of user groups in predicting 2012 U.S. Republican Presidential primaries. In: Social Informatics (2012)

    Google Scholar 

  59. De Choudhury, M., Jhaver, S., Sugar, B., Weber, I.: Social media participation in an activist movement for racial equality. In: ICSWM, pp. 92–101 (2016)

    Google Scholar 

  60. Purohit, H., Hampton, A., Shalin, V.L., Sheth, A.P., Flach, J., Bhatt, S.: What kind of #conversation is twitter? Mining #psycholinguistic cues for emergency coordination. Comput. Hum. Behav. 29, 2438–2447 (2013)

    Article  Google Scholar 

  61. Purohit, H., Hampton, A., Bhatt, S., Shalin, V.L., Sheth, A.P., Flach, J.M.: Identifying seekers and suppliers in social media communities to support crisis coordination. In: Computer Supported Cooperative Work (CSCW) (2014)

    Google Scholar 

  62. Purohit, H., Bhatt, S., Hampton, A., Shalin, V.L., Sheth, A.P.: With whom to coordinate, why and how in ad- hoc social media communications during crisis response. In: Proceedings of the 11th International ISCRAM Conference, pp. 787–791 (2014)

    Google Scholar 

  63. Bhatt, S., Purohit, H., Hampton, A.: Assisting coordination during crisis: a domain ontology based approach to infer resource needs from tweets. In: Web Science (2014)

    Google Scholar 

  64. Nguyen, L.T., Wu, P., Chan, W., Peng, W., Zhang, Y.: Predicting collective sentiment dynamics from time-series social media. In: Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM), pp. 6:1–6:8 (2012)

    Google Scholar 

  65. Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I.: Finki at SemEval-2016 task 4: deep learning architecture for twitter sentiment analysis. In: Proceedings of SemEval, pp. 149–154 (2016)

    Google Scholar 

  66. Esuli, A., Sebastiani, F., Nazionale, C., Ricerche, D.: Optimizing text quantifiers for multivariate loss functions. ACM Trans. Knowl. Discov. Data. VV 26 (2015)

    Google Scholar 

  67. Griffiths, T.L., Steyvers, M., Tenenbaum, J.B.: Topics in semantic representation. Psychol. Rev. (2007)

    Google Scholar 

  68. Chen, L., Org, C., Wang, W., Org, W., Nagarajan, M., Wang, S., Sheth, A.P., Org, A.: Extracting diverse sentiment expressions with target-dependent polarity from twitter. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (2012)

    Google Scholar 

  69. Hong, L., Davison, B.D.: Empirical study of topic modeling in twitter. In: 1st Workshop on Social Media Analytics (SOMA’10) (2010)

    Google Scholar 

  70. Zhao, W.X., Jiang, J., He, J., Song, Y., Achananuparp, P., Lim, E.-P., Li, X.: Topical keyphrase extraction from twitter. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 379–388 (2011)

    Google Scholar 

  71. Wang, X., Gerber, M.S., Brown, D.E.: Automatic crime prediction using events extracted from twitter posts. In: International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. Springer, Berlin (2012)

    Chapter  Google Scholar 

  72. Bhattacharya, N., Arpinar, I., Kursuncu, U.: Real time evaluation of quality of search terms during query expansion for streaming text data using velocity and relevance. In: Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017 (2017)

    Google Scholar 

  73. Phillips, L., Dowling, C., Shaffer, K., Hodas, N., Volkova, S.: Using social media to predict the future: a systematic literature review (2017). Arxiv preprint

    Google Scholar 

  74. Robillard, J.M., Johnson, T.W., Hennessey, C., Beattie, B.L., Illes, J.: Aging 2.0: health information about dementia on twitter. Plos One 20(87) (2013)

    Google Scholar 

  75. Prieto, V.M., Rgio Matos, S., Lvarez, M., Cacheda, F., Oliveira, J.L., Añ, J.A.: Twitter: a good place to detect health conditions. PLoS ONE 9(1) (2014)

    Article  Google Scholar 

  76. Yazdavar, A.H., Al-Olimat, H.S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., Sheth, A.: Semi-supervised approach to monitoring clinical depressive symptoms in social media. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2017)

    Google Scholar 

  77. Coppersmith, G., Dredze, M., Harman, C., Hollingshead Ihmc, K.: From ADHD to SAD: analyzing the language of mental health on twitter through self-reported diagnoses. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp. 1–10 (2015)

    Google Scholar 

  78. Do, T.H., Nguyen, D.M., Tsiligianni, E., Cornelis, B., Deligiannis, N.: Multiview deep learning for predicting twitter users’ location (2017). Arxiv preprint

    Google Scholar 

  79. Lau, J.H., Baldwin, T.: An empirical evaluation of doc2vec with practical insights into document embedding generation (2016). Arxiv preprint

    Google Scholar 

  80. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)

    Google Scholar 

  81. Bo, H., Cook, P., Imoth, T., Dw, B.: Geolocation prediction in social media data by finding location indicative words. In: Proceedings of COLING 2012, pp. 1045–1062 (2012)

    Google Scholar 

  82. Daniulaityte, R., Nahhas, R.W., Wijeratne, S., Carlson, R.G., Lamy, F.R., Martins, S.S., Boyer, E.W., Smith, G.A., Sheth, A.: Time for dabs: analyzing twitter data on marijuana concentrates across the U.S. HHS public access. Drug Alcohol Depend. 155, 307–311 (2015)

    Google Scholar 

  83. Lamy, F.R., Daniulaityte, R., Sheth, A., Nahhas, R.W., Martins, S.S., Boyer, E.W., Carlson Francois R Lamy, R.G.: Those edibles hit hard: exploration of twitter data on cannabis edibles in the U.S HHS public access. Drug Alcohol Depend. 1(164), 64–70 (2016)

    Google Scholar 

  84. Howard, P.N., Hussain, M., Mari, W.: Opening closed regimes what was the role of social media during the Arab Spring? In: Project on Information Technology & Political Islam (2011)

    Google Scholar 

  85. Tufekci, Z.: Big questions for social media big data: representativeness, validity and other methodological pitfalls. In: ICWSM (2014)

    Google Scholar 

  86. Arpinar, I., Kursuncu, U., Achilov, D.: Social media analytics to identify and counter Islamist extremism: systematic detection, evaluation, and challenging of extremist narratives online. In: Proceedings - 2016 International Conference on Collaboration Technologies and Systems, CTS 2016 (2016)

    Google Scholar 

  87. Haciyakupoglu, G., Zhang, W.: Social media and trust during the Gezi protests in turkey. J. Comput. Mediat. Commun. 20(4), 450–466 (2015)

    Article  Google Scholar 

  88. Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2, Spring), 211–236 (2017)

    Google Scholar 

  89. Hoang, T.-A., Cohen, W.W., Lim, E.-P., Pierce, D., Redlawsk, D.P.: Politics, sharing and emotion in microblogs. In: ASONAM (2013)

    Google Scholar 

  90. Makazhanov, A., Rafiei, D.: Predicting political preference of twitter users. Soc. Netw. Anal. Min. (2014)

    Google Scholar 

  91. Cohen, R., Ruths, D.: Classifying political orientation on twitter: it’s not easy! In: ICWSM (2013)

    Google Scholar 

  92. Xu, J.-M., Jun, K.-S., Zhu, X., Bellmore, A.: Learning from bullying traces in social media. In: 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 656–666 (2012)

    Google Scholar 

  93. Chen, Y., Zhu, S., Zhou, Y., Xu, H.: Detecting offensive language in social media to protect adolescent online safety. In: Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Conference on Social Computing (SocialCom) (2012)

    Google Scholar 

  94. Edupuganti, V.: Harassment detection on twitter using conversations. Ph.D. dissertation (2017)

    Google Scholar 

  95. Kandakatla, R.: Identifying offensive videos on YouTube. Ph.D. dissertation (2016)

    Google Scholar 

  96. Wijeratne, S., Doran, D., Sheth, A., Dustin, J.L.: Analyzing the social media footprint of street gangs. In: Intelligence and Security Informatics (ISI) (2015)

    Google Scholar 

  97. Blevins, T., Kwiatkowski, R., Macbeth, J., Mckeown, K., Patton, D., Rambow, O.: Automatically processing tweets from gang-involved youth: towards detecting loss and aggression. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2196–2206 (2016)

    Google Scholar 

  98. Bushman B., Huesmann, L.: Short-term and long-term effects of violent media on aggression in children and adults. Arch. Pediatr. Adolesc. Med. 160, 348–352 (2006)

    Article  Google Scholar 

  99. Ni, M., He, Q., Gao, J.: Using social media to predict traffic flow under special event conditions. In: The 93rd Annual Meeting of Transportation Research Board (2014)

    Google Scholar 

  100. Krishnamurthy, R., Kapanipathi, P., Sheth, A.P., Thirunarayan, K., Sheth, A.: Location prediction of twitter users using wikipedia (2014)

    Google Scholar 

  101. Mahmud, J., Nichols, J., Drews, C.: Where is this tweet from? Inferring home locations of twitter users. In: ICWSM (2012)

    Google Scholar 

  102. Al-Olimat, H.S., Thirunarayan, K., Shalin, V., Sheth, A.: Location name extraction from targeted text streams using Gazeeer-based statistical language models, vol. 11, no. 17 (2017). Arxiv preprint

    Google Scholar 

  103. Haklay, M., Weber, P.: OpenStreetMap: user-generated street maps. IEEE Pers. Commun. 7, 12–18 (2008)

    Google Scholar 

  104. Ahlers, D.: Assessment of the accuracy of GeoNames gazetteer data. In: GIR (2013)

    Google Scholar 

  105. Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia ’ a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web, vol. 1, pp. 1–5 (2012)

    Google Scholar 

  106. Lee, M.D., Lee, M.N.: The relationship between crowd majority and accuracy for binary decisions. Judgm. Decis. Mak. 12(4), 328–343 (2017)

    Google Scholar 

  107. Bhatt, S., Minnery, B., Nadella, S., Bullemer, B., Shalin, V., Sheth, A.: Enhancing crowd wisdom using measures of diversity computed from social media data. In: Proceedings of the International Conference on Web Intelligence (2017)

    Google Scholar 

  108. Smith, A., Gaur, M.: What’s my age?: Predicting twitter user’s age using influential friend network and DBpedia (2018). Arxiv preprint

    Google Scholar 

  109. Refaeilzadeh, P., Tang, L., Liu, H.: Cross-validation. In: Encyclopedia of Database Systems. Springer, Berlin (2009)

    Google Scholar 

  110. Nguyen, D., Smith, N.A., Rosé, C.P.: Author age prediction from text using linear regression. In: Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. Association for Computational Linguistics (2011)

    Google Scholar 

  111. Chen, C., Chang, Y., Ricanek, K., Wang, Y.: Face age estimation using model selection. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp. 93–99 (2010)

    Google Scholar 

  112. Culotta, A., Kumar Ravi, N., Cutler, J.: Predicting twitter user demographics using distant supervision from website traffic data. J. Artif. Intell. Res. 55, 389–408 (2016)

    Article  Google Scholar 

  113. Zhang, J., Hu, X., Zhang, Y., Liu, H.: Your age is no secret: Inferring microbloggers’ ages via content and interaction analysis. In: Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016, pp. 476–485 (2016)

    Google Scholar 

  114. Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: How old do you think i am?”: a study of language and age in twitter. In: ICWSM (2013)

    Google Scholar 

  115. Bamman, D., Eisenstein, J., Schnoebelen, T.: Gender in twitter: styles, stances, and social networks. In: CoRR (2012)

    Google Scholar 

  116. Li, W., Dickinson, M.: Gender prediction for Chinese social media data. In: Proceedings of Recent Advances in Natural Language Processing (2017), pp. 438–445

    Google Scholar 

  117. Li, L., Sun, M., Liu, Z.: Discriminating gender on Chinese microblog: a study of online behaviour, writing style and preferred vocabulary. In: 10th International Conference on Natural Computation (ICNC) (2014)

    Google Scholar 

  118. Volkova, S., Bell, E.: Identifying effective signals to predict deleted and suspended accounts on twitter across languages. In: ICWSM, Association for the Advancement of Artificial Intelligence, pp. 290–298 (2017)

    Google Scholar 

  119. Dickerson, J.P., Kagan, V., Subrahmanian, V.S.: Using sentiment to detect bots on twitter: are humans more opinionated than bots? In: ASONAM (2014)

    Google Scholar 

  120. Varol, O., Ferrara, E., Davis, C.A., Menczer, F., Flammini, A.: Online human-bot interactions: detection, estimation, and characterization. In: ICWSM (2017)

    Google Scholar 

  121. Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684. ACM, New York (2011)

    Google Scholar 

  122. Ross, J., Thirunarayan, K.: Features for ranking tweets based on credibility and newsworthiness. In: International Conference on Collaboration Technologies and Systems (2016)

    Google Scholar 

  123. Gupta, A., Kumaraguru, P., Castillo, C., Meier, P.: TweetCred: a real-time web-based system for assessing credibility of content on twitter. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8851, November 2014

    Google Scholar 

  124. Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. In: PSOSM (2012)

    Google Scholar 

  125. Gupta, A., Lamba, H., Kumaraguru, P., Joshi, A.: Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In: WWW (2013)

    Google Scholar 

  126. Weng, L., Menczer, F., Ahn, Y.-Y.: Predicting successful memes using network and community structure. In: IC, pp. 535–544 (2014)

    Google Scholar 

  127. Kobayashi, R., Lambiotte, R.: TiDeH: time-dependent Hawkes process for predicting retweet dynamics. In: ICWSM, pp. 191–200 (2016)

    Google Scholar 

  128. Tsur, O., Rappoport, A.: Don’t let me be #misunderstood: linguistically motivated algorithm for predicting the popularity of textual memes. In: ICWSM, Ninth International AAAI Conference on Web and Social Media, pp. 426–435 (2015)

    Google Scholar 

  129. Ruan, Y., Purohit, H., Fuhry, D., Parthasarathy, S., Sheth, A.P., Sheth, A.: Prediction of topic volume on twitter. In: 4th International ACM Conference on Web Science, pp. 397–402 (2012)

    Google Scholar 

  130. Pattisapu, N., Gupta, M., Kumaraguru, P., Varma, V.: Medical persona classification in social media. In: ASONAM (2017)

    Google Scholar 

  131. Gilani, Z., Kochmar, E., Crowcroft, J.: Classification of twitter accounts into automated agents and human users. In: ASONAM (2017)

    Google Scholar 

  132. Alowibdi, J.S., Buy, U.A., Yu, P.S., Stenneth, L.: Detecting deception in online social networks. In: ASONAM (2014)

    Google Scholar 

  133. Mahmud, J., Fei, G., Xu, A., Pal, A., Zhou, M.: Predicting attitude and actions of twitter users. In: Proceedings of the 21st International Conference on Intelligent User Interfaces - IUI’16, pp. 1–6. ACM, New York (2016)

    Google Scholar 

  134. Georgiev, P., Noulas, A., Mascolo, C.: Where businesses thrive: predicting the impact of the olympic games on local retailers through location-based services data, pp. 151–160. In: ICWSM (2014)

    Google Scholar 

  135. Yang, X., Mccreadie, R., Macdonald, C., Ounis, I.: Transfer learning for multi-language twitter election classification. In: ASONAM (2017)

    Google Scholar 

  136. Korolov, R., Lu, D., Wang, J., Zhou, G., Bonial, C., Voss, C., Kaplan, L., Wallace, W., Han, J., Ji, H.: On predicting social unrest using social media. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)

    Google Scholar 

  137. Kallus, N.: Predicting crowd behavior with big public data. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 625–630. ACM, New York (2014)

    Google Scholar 

  138. Echeverria, J., Zhou, S.: Discovery, retrieval, and analysis of the ‘star wars’ botnet in twitter. In: ASONAM (2017)

    Google Scholar 

  139. Gao, W., Sebastiani, F.: Tweet sentiment: from classification to quantification. In: ASONAM (2015)

    Google Scholar 

  140. Hassan, A., Abbasi, A., Zeng, D.: Twitter sentiment analysis: a bootstrap ensemble framework. In: SocialCom (2013)

    Google Scholar 

  141. Kothari, A., Magdy, W., Darwish, K., Mourad, A., Taei, A.: Detecting comments on news articles in microblogs. In: ICWSM (2013)

    Google Scholar 

  142. Georgiou, T., Abbadi, A.E., Yan, X., George, J.: Mining complaints for traffic-jam estimation: a social sensor application. In: ASONAM (2015)

    Google Scholar 

  143. Aiswal, A.J., Peng, W., Sun, T.: Predicting time-sensitive user locations from social media. In: ASONAM (2013)

    Google Scholar 

  144. Rout, D., Preoiuc-Pietro, D., Bontcheva, K., Cohn, T.: Where’s @wally? A classification approach to geolocating users based on their social ties. In: 24th ACM Conference on Hypertext and Social Media, Paris (2013)

    Google Scholar 

  145. Rath, B., Gao, W., Ma, J., Srivastava, J.: From retweet to believability: utilizing trust to identify rumor spreaders on twitter. In: ASONAM (2017)

    Google Scholar 

  146. Bizid, I., Nayef, N., Boursier, P., Faiz, S., Morcos, J.: Prominent users detection during specific events by learning on-and off-topic features of user activities. In: ASONAM (2015)

    Google Scholar 

  147. Ferrara, E., Jafariasbagh, M., Varol, O., Qazvinian, V., Menczer, F., Flammini, A.: Clustering memes in social media. In: ASONAM (2013)

    Google Scholar 

  148. Yamamoto, S., Satoh, T.: Hierarchical estimation framework of multi-label classifying: a case of tweets classifying into real life aspects. In: ICWSM (2015)

    Google Scholar 

  149. Beykikhoshk, A., Arandjelovi, O., Phung, D., Venkatesh, S.: Data-mining twitter and the autism spectrum disorder: a pilot study. In: ASONAM (2014)

    Google Scholar 

  150. Yin, Z., Chen, Y., Fabbri, D., Sun, J., Malin, B.: #PrayForDad: learning the semantics behind why social media users disclose health information. In: ICWSM (2016)

    Google Scholar 

  151. Daniulaityte, R., Chen, L., Lamy, F.R., Carlson, R.G., Thirunarayan, K., Sheth, A.: ‘When ‘bad’ is ‘good’: identifying personal communication and sentiment in drug-related tweets. JMIR Public Health Surveill. (2016)

    Google Scholar 

  152. Hu, Y., Farnham, S., Talamadupula, K.: Predicting user engagement on twitter with real-world events. In: ICWSM (2015)

    Google Scholar 

  153. Kessler, J.S., Eckert, M., Clark, L., Nicolov Power, N.J.: The ICWSM 2010 JDPA sentiment corpus for the automotive domain. In: 4th International AAAI Conference on Weblogs and Social Media Data Workshop Challenge (ICWSM-DWC) (2010)

    Google Scholar 

  154. Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems (2016)

    Google Scholar 

  155. Korpusik, M., Sakaki, S., Chen, F., Chen, Y.-Y.: Recurrent neural networks for customer purchase prediction on twitter. In: CBRecSys@ RecSys, pp. 47–50 (2016)

    Google Scholar 

  156. Tieleman, T., Hinton, G.: Divide the gradient by a running average of its recent magnitude. In: COURSERA: Neural Networks for Machine Learning (2012)

    Google Scholar 

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Acknowledgements

We are grateful to Amelie Gyrard, Mustafa Nural, Sanjaya Wijeratne, Shreyansh Bhatt, and Ankita Saxena for their assistance with their reviews and comments that greatly improved this book chapter.

We acknowledge partial support from the National Science Foundation (NSF) award CNS-1513721: “Context-Aware Harassment Detection on Social Media,” National Institutes of Health (NIH) award: MH105384-01A1: “Modeling Social Behavior for Healthcare Utilization in Depression,” NSF award EAR- 1520870 ‘Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response’, Community in Social Media: This work was supported by Army Research Office Grant No. W911NF-16-1-0300, National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02 Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use. Any opinions, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF, NIH, NIDA, or Army Research Office.

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Kursuncu, U., Gaur, M., Lokala, U., Thirunarayan, K., Sheth, A., Arpinar, I.B. (2019). Predictive Analysis on Twitter: Techniques and Applications. In: Agarwal, N., Dokoohaki, N., Tokdemir, S. (eds) Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-94105-9_4

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