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Characterizing and Detecting Social Outrage on Twitter: Patel Reservation in Gujarat

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Data Science and Analytics (REDSET 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 799))

Abstract

Social Media is a platform to share ideas, opinions and discussions. This provides scope to study social behavior and perform analysis around events discussed over it. The idea behind this study is to analyze the social characteristics during unrest in society. The analysis further can be used to identify the trend of social behavior and utilize for decision making and anticipatory governance. For this paper recent social outrage in Indian context related to caste based reservation has been studied using social media platform Twitter. A number of analytical methodologies have been used to understand the variations in opinions over social media during unrest. This paper researches the potential of tension during social outrage and the factors affecting it. Sentiment analysis and different machine learning methods used to detect level of tension and compared the results against manual annotation. To improve the performance of classification results, a rule based algorithm has been developed to detect tension during social outrage.

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Notes

  1. 1.

    http://www.annahazare.org/anticorruption-movement.html.

  2. 2.

    http://indianexpress.com/article/india/india-news-india/jnu-agitation-questioned-by-police-for-talking-to-students-under-scanner-five-journalists-speak-out.

  3. 3.

    http://www.financialexpress.com/industry/at-mega-patel-mega-rally-for-obc-quota-demand-hardik-patel-warns-bjp-govt-of-consequences/125651/.

  4. 4.

    http://textblob.readthedocs.io/en/dev/quickstart.html#sentiment-analysis.

  5. 5.

    http://www.nltk.org/book/ch03.html.

  6. 6.

    http://www.cs.waikato.ac.nz/ml/weka/.

References

  1. Poell, T., Borra, E.: Twitter, YouTube and Flickr as platforms of alternative journalism: the social media account of the 2010 Toronto G20 protests. SAGE J. (2011). https://doi.org/10.1177/1464884911431533. sagepub.co.uk/journalsPermissions.nav

  2. Eltantawy, N., Wiest, J.B.: Social media in the Egyptian revolution: reconsidering resource mobilization theory. Int. J. Commun. 5, 1207–1224 (2011). 1932–8036/2011FEA1207

    Google Scholar 

  3. Bajpai, K., Jaiswal, A.: A framework for analyzing collective action events on Twitter. In: 8th International ISCRAM Conference, Lisbon, Portugal, May 2011

    Google Scholar 

  4. Bruns, A., Stieglitz, S.: Quantitative approaches to comparing communication patterns on Twitter. J. Technol. Hum. Serv. 30(3–4) (2012). https://doi.org/10.1080/15228835.2012.744249. Methods for Analyzing Social Media

  5. Compton, R., Lee, C., Lu, T.-C., De Silva, L., Macy, M.: Detecting future social unrest in unprocessed Twitter data: “Emerging phenomena and big data”. In: IEEE International Conference on Intelligence and Security Informatics (ISI) (2013). https://doi.org/10.1109/isi.2013.6578786

  6. Panagiotopoulosa, P., Bigdelib, A.Z., Samsa, S.: Citizen–government collaboration on social media: the case of Twitter in the 2011 riots in England. Gov. Inf. Q. J. 31, July 2014. https://doi.org/10.1016/j.giq.2013.10.014. ISSN 0740-624X

  7. Recuero, R., Zago, G., Bastos, M.T., Araújo, R.: Hashtags functions in the protests across Brazil. SAGE J., May 2015. https://doi.org/10.1177/2158244015586000

  8. Compton, R., Jurgens, D., Allen, D.: Geotagging one hundred million Twitter accounts with total variation minimization. In: IEEE International Conference (2014). https://doi.org/10.1109/bigdata.2014.7004256

  9. Burnapa, P., Ranaa, O.F., Avisa, N., Williams, M., Housley, W., Edwards, A., Morgan, J., Sloan, L.: Detecting tension in online communities with computational Twitter analysis. Technol. Forecast. Soc. Change J. 95, 96–108 (2013). https://doi.org/10.1016/j.techfore.2013.04.013

    Article  Google Scholar 

  10. Aday, S., Farrell, H., Lynch, M., Sides, J., Freelon, D.: New Media and Conflict After the Arab Spring. United States Institute of Peace (2012)

    Google Scholar 

  11. Xu, J., Lu, T.-C., Compton, R., Allen, D.: Civil unrest prediction: a Tumblr-based exploration. In: International Social Computing, Behavioral Modeling and Prediction (SBP) Conference (2014)

    Google Scholar 

  12. Kallus, N.: Predicting crowd behavior with big public data. In: International World Wide Web Conference Committee (IW3C2), WWW 2014 Companion, Seoul, Korea, 7–11 April 2014. ACM (2014). 978-1-4503-2745-9/14/04

    Google Scholar 

  13. Lerman, K., Ghosh, R.: Information contagion: an empirical study of the spread of news on Digg and Twitter social networks. In: Fourth International AAAI Conference on Weblogs and Social Media, March 2010

    Google Scholar 

  14. McHugh, M.L.: Interrater reliability: the kappa statistic. US National Library of Medicine National Institute of Health, October 2012

    Google Scholar 

  15. Ezpeleta, E., Zurutuza, U., Gomez Hidalgo, J.M.: Does sentiment analysis help in Bayesian spam filtering? In: International Conference on Hybrid Artificial Intelligent Systems, April 2016. https://doi.org/10.1007/978-3-319-32034-2_7

  16. Lakshmi Devasena, C.: Comparative analysis of random forest, REP tree and J48 classifiers for credit risk prediction. In: IJCA Proceedings on International Conference on Communication, Computing and Information Technology (2015)

    Google Scholar 

  17. 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 

  18. Hua, T., Lu, C.-T., Ramakrishnan, N., Chen, F., Arredondo, J., Mares, D., Summers, K.: Analyzing civil unrest through social media. IEEE J. Mag. 46(12) (2013). https://doi.org/10.1109/mc.2013.442

  19. Rathi, D., Given. L.: Research 2.0: framework for qualitative and quantitative research in Web 2.0 environments. In: 43rd International Conference on System Sciences (2010)

    Google Scholar 

  20. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37, 267–307 (2011)

    Article  Google Scholar 

  21. Sumner, C., Byers, A., Boochever, R., Park, G.J.: Predicting dark triad personality traits from Twitter usage and a linguistic analysis of Tweets. In: Proceedings of the 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, vol. 02. IEEE Computer Society, Washington, D.C. (2012). https://doi.org/10.1109/icmla.2012.218

  22. Barbosa, L., Feng, J.: Robust sentiment detection on Twitter from biased and noisy data. In: 23rd International Conference on Computational Linguistics. Association for Computational Linguistics (2010)

    Google Scholar 

  23. Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC 2010), May 2010

    Google Scholar 

  24. Chen, F., Neill, D.B.: Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2014. https://doi.org/10.1145/2623330.2623619

  25. Ma, J., Gao, W., Wei, Z., Lu, Y., Wong, K.-F.: Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (2015). https://doi.org/10.1145/2806416.2806607

  26. Krishna Kumar, K.P., Geethakumari, G.: Detecting misinformation in online social networks using cognitive psychology. Hum. Centric Comput. Inf. Sci. (2014). https://doi.org/10.1186/s13673-014-0014-x

  27. Xu, Z., Liu, Y., Xuan, J., Chen, H., Mei, L.: Crowd-sourcing based social media data analysis of urban emergency events. Multimedia Tools Appl. 76(9), 11567–11584 (2017). https://doi.org/10.1007/s11042-015-2731-1

    Article  Google Scholar 

  28. Aggarwal, C.C., Subbian, K.: Event detection in social streams. In: Proceedings of the 2012 SIAM International Conference on Data Mining (2012). https://doi.org/10.1137/1.9781611972825.54

  29. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC 2010), May 2010

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi, India

    Sulbha Singh

  2. Capita plc, London, UK

    Rajeev Pal

Authors
  1. Sulbha Singh
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  2. Rajeev Pal
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Corresponding author

Correspondence to Sulbha Singh .

Editor information

Editors and Affiliations

  1. University of Arkansas, Fayetteville, AR, USA

    Brajendra Panda

  2. GD Goenka University, Gurugram, Haryana, India

    Sudeep Sharma

  3. GD Goenka University, Gurugram, Haryana, India

    Nihar Ranjan Roy

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Singh, S., Pal, R. (2018). Characterizing and Detecting Social Outrage on Twitter: Patel Reservation in Gujarat. In: Panda, B., Sharma, S., Roy, N. (eds) Data Science and Analytics. REDSET 2017. Communications in Computer and Information Science, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-10-8527-7_42

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  • DOI: https://doi.org/10.1007/978-981-10-8527-7_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8526-0

  • Online ISBN: 978-981-10-8527-7

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