(Received: 2019-07-09, Revised: 2019-08-15 , Accepted: 2019-09-03)
Given the tremendous growth of social media platforms, people have been actively spreading not only information in general, but also political opinions. Many research efforts have used social media content to analyse and predict the public opinion towards political events. This work presents an analytical study for measuring the political public opinion towards the Palestinian-Israeli conflict by using Twitter data. The study uses a novel data analysis model that leverages two levels of analysis: country-level analysis and individual-level analysis. The country-level analysis aims to explore the country's overall attitude towards Palestine by: 1) Identifying counties that generated the most topic-focused tweets, 2) Measuring the friendliness of each country towards Palestine. 3) Analysing the change of sentiment over time. The individual-level analysis aims to analyse data based on the activity and background of individuals. The attitudes of opinion leaders and ethnic groups were analysed and discussed in light of countries' attitudes. The rich experience provided in this study through the proposed model for multi-level analysis, the step-by-step procedure, the variety of analysis techniques and the discussion of results can be informative for other developers and data analysts who are interested in analysing social media sentiment about political conflicts in particular.
  1. A. Bermingham and A. Smeaton, "On Using Twitter to Monitor Political Sentiment and Predict Election Results," Proceedings of the Workshop on Sentiment Analysis Where AI Meets Psychology (SAAIP 2011), pp. 2-10, 2011.
  2. A. Tumasjan, T. O. Sprenger, P. G.Sandner and I. M. Welpe, "Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment," Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, Association for the Advancement of Artificial Intelligence, pp. 178-185, 2010.
  3. A. Jungherr, "Twitter Use in Election Campaigns: A Systematic Literature Review," Journal of Information Technology & Politics, vol. 13, no. 1, pp. 72-91, 2016.
  4. B. O'Connor, R. Balasubramanyan, B. R. Routledge and N. A. Smith, "From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series," Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, Association for the Advancement of Artificial Intelligence, pp. 122-129, 2010.
  5. H. Wang, D. Can, A. Kazemzadeh, F. Bar and S. Narayanan, "A System for Real-time Twitter Sentiment Analysis of 2012 US Presidential Election Cycle," Association for Computational Linguistics, pp. 115- 120, 2012.
  6. E. Siapera, "Tweeting# Palestine: Twitter and the Mediation of Palestine," International Journal of Cultural Studies, vol. 17, no. 6, pp. 539-555, 2014.
  7. E. Siapera, G. Hunt and T. Lynn, "# GazaUnderAttack: Twitter, Palestine and Diffused War," Information, Communication & Society, vol. 18, no. 11, pp. 1297-1319, 2015.
  8. P. Sobkowicz, M. Kaschesky and G. Bouchard, "Opinion Mining in Social Media: Modeling, Simulating and Forecasting Political Opinions in the Web," Government Information Quarterly, vol. 29, no. 4, pp. 470-479, 2012.
  9. J. A. Balazs and J. D. Velásquez, "Opinion Mining and Information Fusion: A Survey," Information Fusion, vol. 27, pp. 95-110, 2016.
  10. E. Martínez-Cámara, M. T. Martín-Valdivia, L. A. Urena-López and A. R. Montejo-Ráez, "Sentiment Analysis in Twitter," Natural Language Engineering, vol. 20, no. 1, pp. 1-28, 2014.
  11. F. Bellini and N. Fiore, "Exploring Sentiment on Financial Market Through Social Media Stream Analysis," Reshaping Accounting and Management Control Systems, Springer, pp. 115-129, 2017.
  12. M. Nardo, M. Petracco-Giudici and M. Naltsidis, "Walking Down Wall Street with a Tablet: A Survey of Stock Market Predictions Using the Web," Journal of Economic Surveys, vol. 30, no. 2, pp. 356-369, 2016.
  13. J. Bollen, H. Mao and X. Zeng, "Twitter Mood Predicts the Stock Market," Journal of Computational Science, vol. 2, no. 1, pp. 1-8, 2011.
  14. P. N. Howard, G. Bolsover, B. Kollanyi, S. Bradshaw and L.-M. Neudert, "Junk News and Bots during the US Election: What Were Michigan Voters Sharing over Twitter," Computational Propaganda Research Project, Oxford Internet Institute, Data Memo, 2017.
  15. D. Murthy, "Twitter and Elections: Are Tweets, Predictive, Reactive or a Form of Buzz?," Information, Communication & Society, vol. 18, no. 7, pp. 816-831, 2015.
  16. P. L. Francia, "Free Media and Twitter in the 2016 Presidential Election: The Unconventional Campaign of Donald Trump," Social Science Computer Review, vol. 36, no. 4, pp. 440-455, 2018.
  17. D. Gayo-Avello, "No, You Cannot Predict Elections with Twitter," IEEE Internet Computing, vol. 16, no. 6, pp. 91-94, 2012.
  18. P. Howard, B. Kollanyi and S. C. Woolley, "Bots and Automation over Twitter during the Second US Presidential Debate," The Computational Propaganda Project, Oxford Internet Institute, pp. 1-4, 2016.
  19. D. Freelon and D. Karpf, "Of Big Birds and Bayonets: Hybrid Twitter Interactivity in the 2012 Presidential Debates," Information, Communication & Society, vol. 18, no. 4, pp. 390-406, 2015.
  20. K. Gorkovenko and N. Taylor, "Understanding How People Use Twitter during Election Debates," Proceedings of the ACM 31st British Computer Society Human Computer Interaction Conference, BCS Learning & Development, Ltd., pp. 88, 2017.
  21. D. Kreiss, "Seizing the Moment: The Presidential Campaigns’ Use of Twitter during the 2012 Electoral Cycle," New Media & Society, vol. 18, no. 8, pp. 1473-1490, 2016.
  22. T. Lansdall-Welfare, F. Dzogang and N. Cristianini, "Change-point Analysis of the Public Mood in UK Twitter during the Brexit Referendum," Proc. of IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 434-439, 2016.
  23. N. A. Diakopoulos and D. A. Shamma, "Characterizing Debate Performance via Aggregated Twitter Sentiment," Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, pp. 1195-1198, 2010.
  24. S. Stieglitz and L. Dang-Xuan, "Political Communication and Influence through Microblogging: An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior," Proc. of the 45th IEEE Hawaii International Conference on System Sciences, pp. 3500-3509, 2012.
  25. I. Alfina, D. Sigmawaty, F. Nurhidayati and A. N. Hidayanto, "Utilizing Hashtags for Sentiment Analysis of Tweets in the Political Domain," Proceedings of the 9th International Conference on Machine Learning and Computing, ACM, pp. 43-47, 2017.
  26. P. Grover, A. K. Kar, Y. K. Dwivedi and M. Janssen, "Polarization and Acculturation in US Election 2016 Outcomes–Can Twitter Analytics Predict Changes in Voting Preferences," Technological Forecasting and Social Change, pp. 1-23, 2018.
  27. R. Bose, R. K. Dey, S. Roy and D. Sarddar, "Analyzing Political Sentiment Using Twitter Data," Information and Communication Technology for Intelligent Systems, Springer, pp. 427-436, 2019.
  28. D. Paul, F. Li, M. K. Teja, X. Yu and R. Frost, "Compass: Spatio Temporal Sentiment Analysis of US Eelection What Twitter Says!," Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 1585-1594, 2017.
  29. O. Almatrafi, S. Parack and B. Chavan, "Application of Location-based Sentiment Analysis Using Twitter for Identifying Trends towards Indian General Elections 2014," Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, ACM Edn., 2015.
  30. S. Vosoughi, H. Zhou and D. Roy, "Enhanced Twitter Sentiment Classification Using Contextual Information," arXiv preprint arXiv:1605.05195, 2016.
  31. J. Deegan, J. Hogan, S. Feeney and B. K. O'Rourke, "The Self and Other: Portraying Israeli and Palestinian Identities on Twitter," Irish Communication Review, vol. 16, no. 1, Article 8, 2018.
  32. J. W. Pennebaker, M. E. Francis and R. J. Booth, "Linguistic Inquiry and Word Count: LIWC 2001," Mahway: Lawrence Erlbaum Associates, vol. 71, 2001.
  33. P. Burnap, R. Gibson, L. Sloan, R. Southern and M. Williams, "140 Characters to Victory?: Using Twitter to Predict the UK 2015 General Election," Electoral Studies, vol. 41, pp. 230-233, 2016.
  34. A. Ceron, L. Curini and S. M. Iacus, "Using Sentiment Analysis to Monitor Electoral Campaigns: Method Matters—Evidence from the US and Italy," Soci. Sci. Comp. Review, vol. 33, no. 1, pp. 3-20, 2015.
  35. A. Ceron, L. Curini and S. M. Iacus, "To What Extent Sentiment Analysis of Twitter Is Able to Forecast Electoral Results? Evidence from France, Italy and the United States," Proc. of the 7th ECPR General Conference Sciences Po, Bordeaux, pp. 5-8, 2013.
  36. D. J. Hopkins and G. King, "A Method of Automated Non-parametric Content Analysis for Social Science," American Journal of Political Science, vol. 54, no. 1, pp. 229-247, 2010.
  37. H. T. Le, G. Boynton, Y. Mejova, Z. Shafiq and P. Srinivasan, "Revisiting the American Voter on Twitter," Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM, pp. 4507-4519, 2017.
  38. F. Marozzo and A. Bessi, "Analyzing Polarization of Social Media Users and News Sites during Political Campaigns," Social Network Analysis and Mining, Springer, vol. 8, no. 1, 2018.
  39. S. Martin-Gutierrez, J. C. Losada and R. M. Benito, "Recurrent Patterns of User Behavior in Different Electoral Campaigns: A Twitter Analysis of the Spanish General Elections of 2015 and 2016," Complexity Jour., vol. 2018, 2018.
  40. E. M. Cody, A. J. Reagan, L. Mitchell, P. S. Dodds and C. M. Danforth, "Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll," PloS One, vol. 10, no. 8, pp. e0136092, 2015.
  41. N. S. Khan, M. Ata and Q. Rajput, "Identification of Opinion Leaders in Social Network," Proc. of IEEE International Conference on Information and Communication Technologies (ICICT), pp. 1-6, 2015.
  42. F. Bodendorf and C. Kaiser, "Detecting Opinion Leaders and Trends in Online Social Networks," Proceedings of the 2nd ACM Workshop on Social Web Search and Mining, pp. 65-68, 2009.
  43. M. Solomon, R. Russell-Bennett and J. Previte, Consumer Behaviour, Pearson Higher Education AU, 3rd Edition , 2012.
  44. F. Riquelme and P. González-Cantergiani, "Measuring User Influence on Twitter: A Survey," Information Processing & Management, vol. 52, no. 5, pp. 949-975, 2016.
  45. M. Gaurav, A. Srivastava, A. Kumar and S. Miller, "Leveraging Candidate Popularity on Twitter to Predict Election Outcome," Proceedings of the 7th Workshop on Social Network Mining and Analysis, ACM, Article 7, 2013.
  46. DNOiSE., "Followthehashtag // Free twitter search analytics and business intelligence tool,"[Online], Available:
  47. O. Owoputi, B. O'Connor, C. Dyer, K. Gimpel, N. Schneider and N. A. Smith, "Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters," Proceedings of NAACL-HLT, Association for Computational Linguistics, pp. 380–390, 2013.
  48. K. Gimpel, N. Schneider and B. O’Connor, "Annotation Guidelines for Twitter Part-of-Speech Tagging Version 0.3,"[Online], Available:", 2013.
  49. M. J. Idzelis, "The Java Open Source Spell Checker,"[Online], Available:,[Accessed: 15-08-2019].
  50. Alias-i., "LingPipe 4.1.0.,"[Online], Available:,[Accessed: 15-08-2019].
  51. C. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. Bethard and D. McClosky, "The Stanford CoreNLP Natural Language Processing Toolkit," pp. 55-60, DOI:10.3115/v1/p14-5010, 2014.
  52. M. Thelwall, K. Buckley, G. Paltoglou, C. Cai and A. Kappas, "SentiStrength,"[Online], Available:,[Accessed: 15-08-2019].
  53. R. Kumar, H. S. Pannu and A. K. Malhi, "Aspect-based Sentiment Analysis Using Deep Networks and Stochastic Optimization," Neural Computing and Applications, pp. 1-15, 2019.
  54. A. Valdivia, M. V. Luzón, E. Cambria and F. Herrera, "Consensus Vote Models for Detecting and Filtering Neutrality in Sentiment Analysis," Information Fusion, vol. 44, pp. 126-135, 2018.
  55. A. Milani, N. Rajdeep, N. Mangal, R. K. Mudgal and V. Franzoni, "Sentiment Extraction and Classification for the Analysis of Users’ Interest in Tweets," International Journal of Web Information Systems, vol. 14, no. 1, pp. 29-40, 2018.
  56. E. Kušen and M. Strembeck, "Politics, Sentiments and Misinformation: An Analysis of the Twitter Discussion on the 2016 Austrian Presidential Elections," Online Social Networks and Media, vol. 5, pp. 37-50, 2018.
  57. M. Bouazizi and T. Ohtsuki, "Sentiment Analysis: From Binary to Multi-class Classification: A Patternbased Approach for Multi-class Sentiment Analysis in Twitter," IEEE Access, vol. 5, pp. 1-6, 2016.
  58. U. Yaqub, S. Chun, V. Atluri and J. Vaidya, "Sentiment-based Analysis of Tweets during the US Presidential Elections," Proceedings of the 18th Annual International Conference on Digital Government Research, ACM, pp. 1-10, 2017.
  59., "Apache Spark: Unified Analytics Engine for Big Data,"[Online], Available:,[Accessed: 15/08/2019].
  60. L. Bornmann and R. Haunschild, "How to Normalize Twitter Counts? A First Attempt Based on Journals in the Twitter Index," Scientometrics, vol. 107, no. 3, pp. 1405-1422, 2016.
  61. R. Taylor, "Interpretation of the Correlation Coefficient: A Basic Review," Journal of Diagnostic Medical Sonography, vol. 6, no. 1, pp. 35-39, 1990.
  62. E. Dubois and D. Gaffney, "The Multiple Facets of Influence: Identifying Political Influentials and Opinion Leaders on Twitter," American Behavioral Scientist, vol. 58, no. 10, pp. 1260-1277, 2014.
  63. W. W. Xu, Y. Sang, S. Blasiola and H. W. Park, "Predicting Opinion Leaders in Twitter Activism Networks: The Case of the Wisconsin Recall Election," American Behavioral Scientist, vol. 58, no. 10, pp. 1278-1293, 2014.
  64. D. S. Moore, G. P. McCabe and B. A. Craig, Introduction to the Practice of Statistics, 7th Edition, W.H. Freeman & Company, 2012.