Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Scientific Reports

Volumn 8, Issue 1, 2018, Pages

  Bovet, Alexandre ^a   Morone, Flaviano ^a   Makse, Hernán A ^a  

^a CITY COLLEGE OF NEW YORK   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ELECTION; HUMAN; MACHINE LEARNING; NEW YORK; PHYSICS; SOCIAL NETWORK; VALIDATION PROCESS;

EID: 85048225185     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-018-26951-y     Document Type: Article

References (64)

1. Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P. & Rosenquist, J. N. Pulse of the nation: US mood throughout the day inferred from Twitter http://www.ccs.neu.edu/home/amislove/twittermood/(2010).
2. Hannak, A. et al. Tweetin' in the rain: exploring societal-scale effects of weather on mood. In Proc. 6th Int. AAAI Conf. Weblogs Soc. Media, 479-482 (Dublin, Ireland, 2012).
3. Pak, A. & Paroubek, P. Twitter as a corpus for sentiment analysis and opinion mining. In Proc. Seventh Int. Conf. Lang. Resour. Eval., pp. 19-21 (European Language Resources Association (ELRA), Valletta, Malta, 2010).
4. Quattrociocchi, W., Caldarelli, G. & Scala, A. Opinion dynamics on interacting networks: media competition and social influence. Sci. Rep. 4, 4938, https://doi.org/10.1038/srep04938 (2014).
5. Liu, Y., Huang, X., An, A. & Yu, X. ARSA: a sentiment-aware model for predicting sales performance using blogs. In Proc. 30th Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., 607 (ACM Press, New York, New York, USA, https://doi.org/10.1145/1277741.1277845 (2007).
6. Bollen, J., Mao, H. & Zeng, X. Twitter mood predicts the stock market. J. Comput. Sci. 2, 1-8, https://doi.org/10.1016/j.jocs.2010.12.007 (2011).
7. Zheludev, I., Smith, R. & Aste, T. When can social media lead financial markets? Sci. Rep. 4, 4213, https://doi.org/10.1038/srep04213 (2014).
8. Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M. & Mozetič, I. The effects of twitter sentiment on stock price returns. PLoS One 10, e0138441, https://doi.org/10.1371/journal.pone.0138441 (2015).
9. Curme, C., Stanley, H. E. & Vodenska, I. Coupled network approach to predictability of financial market returns and news sentiments. Int. J. Theor. Appl. Financ. 18, 1550043, https://doi.org/10.1142/S0219024915500430 (2015).
10. O'Connor, B., Balasubramanyan, R., Routledge, B. R. & Smith, N. a. From tweets to polls: Linking text sentiment to public opinion time series. 122-129, DOI:citeulike-article-id:7044833 (2010).
11. Tumasjan, A., Sprenger, T. O., Sandner, P. G. & Welpe, I. M. Election forecasts with Twitter: how 140 characters reflect the political landscape. Soc. Sci. Comput. Rev. 29, 402-418, https://doi.org/10.1177/0894439310386557 (2011).
12. Shi, L., Agarwal, N., Agrawal, A., Garg, R. & Spoelstra, J. Predicting US primary elections with Twitter. In Proc. Work. Soc. Netw. Soc. Media Anal., 1-8 (Lake Tahoe, Nevada, 2012).
13. Marchetti-Bowick, M. & Chambers, N. Learning for microblogs with distant supervision: political forecasting with Twitter. In Proc. 13th Conf. Eur. Chapter Assosciation Comput. Linguist. 603-612 (2012).
14. Borondo, J., Morales, A. J., Losada, J. C. & Benito, R. M. Characterizing and modeling an electoral campaign in the context of Twitter: 2011 spanish presidential election as a case study. Chaos 22, 023138, https://doi.org/10.1063/1.4729139 (2012).
15. Park, S., Ko, M., Lee, J. & Song, J. Agenda diversity in social media discourse: a study of the 2012 korean general election. In Proc. 7th Int. Conf. Web Soc. Media 486-495 (2012).
16. Contractor, D. & Faruquie, T. A. Understanding election candidate approval ratings using social media data. In Proc. 22nd Int. Conf. World Wide Web - WWW '13 Companion, 189-190 (ACM Press, New York, New York, USA, https://doi.org/10.1145/2487788.2487883 (2013).
17. Thapen, N. A. & Ghanem, M. M. Towards passive political opinion polling using twitter. In CEUR Workshop Proc., vol. 1110, 19-34 (2013).
18. Hoang, T.-A., Cohen, W. W., Lim, E.-P., Pierce, D. & Redlawsk, D. P. Politics, sharing and emotion in microblogs. In Proc. 2013 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Min. 282-289 (ACM Press, New York, New York, USA, https://doi.org/10.1145/2492517.2492554 (2013).
19. Fink, C., Bos, N., Perrone, A., Liu, E. & Kopecky, J. Twitter, public opinion, and the 2011 nigerian presidential election. In 2013 Int. Conf. Soc. Comput. 311-320, https://doi.org/10.1109/SocialCom.2013.50 (IEEE, 2013).
20. Gayo-Avello, D. A meta-analysis of state-of-the-art electoral prediction from Twitter data. Soc. Sci. Comput. Rev. 31, 649-679, https://doi.org/10.1177/0894439313493979 (2013).
21. Caldarelli, G. et al. A multi-level geographical study of italian political elections from twitter data. PLoS One 9, e95809, https://doi.org/10.1371/journal.pone.0095809 (2014).
22. Borge-Holthoefer, J., Magdy, W., Darwish, K. & Weber, I. Content and network dynamics behind egyptian political polarization on twitter. In Proc. 18th ACM Conf. Comput. Support. Coop. Work Soc. Comput. 700-711 (ACM Press, New York, New York, USA, https://doi.org/10.1145/2675133.2675163 (2015).
23. Tsakalidis, A., Papadopoulos, S., Cristea, A. I. & Kompatsiaris, Y. Predicting elections for multiple countries using twitter and polls. IEEE Intell. Syst. 30, 10-17, https://doi.org/10.1109/MIS.2015.17 (2015).
24. Kagan, V., Stevens, A. & Subrahmanian, V. Using twitter sentiment to forecast the 2013 pakistani election and the 2014 indian election. IEEE Intell. Syst. 30, 2-5, https://doi.org/10.1109/MIS.2015.16 (2015).
25. Saifuddin, A., Kokil, J. & Marko, M. S. Tweets & votes - a 4 country comparison of volumetric and sentiment analysis approcahes. In Proc. 10th Int. Conf. Web Soc. Media, 507-510 (2016).
26. Wang, Y., Li, Y. & Luo, J. Deciphering the 2016 U.S. presidential campaign in the Twitter sphere: a comparison of the trumpists and clintonists. In Proc. 10th Int. Conf. Web Soc. Media, 4 (2016).
27. Llewellyn, C. & Cram, L. Brexit? analyzing opinion on the uk-eu referendum within Twitter. In Proc. 10th Int. Conf. Web Soc. Media, 760-761 (2016).
28. Jungherr, A., Jurgens, P. & Schoen, H. Why the pirate party won the german election of 2009 or the trouble with predictions: a response to Tumasjan, A., Sprenger, T. O., Sander, P. G. & Welpe, I. M. "predicting elections with Twitter: what 140 characters reveal about political sentiment". Soc. Sci. Comput. Rev. 30, 229-234, https://doi.org/10.1177/0894439311404119 (2012).
29. Jungherr, A., Schoen, H., Posegga, O. & Jurgens, P. Digital trace data in the study of public opinion: an indicator of attention toward politics rather than political support. Soc. Sci. Comput. Rev. 0894439316631043 (2016).
30. Subrahmanian, V. & Reforgiato, D. AVA: adjective-verb-adverb combinations for sentiment analysis. IEEE Intell. Syst. 23, 43-50, https://doi.org/10.1109/MIS.2008.57 (2008).
31. Montejo-Ráez, A., Martínez-Cámara, E., Martín-Valdivia, M. T. & Ureña-López, L. A. Ranked WordNet graph for sentiment polarity classification in Twitter. Comput. Speech Lang. 28, 93-107, https://doi.org/10.1016/j.csl.2013.04.001 (2014).
32. Tausczik, Y. R. & Pennebaker, J. W. The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29, 24-54, https://doi.org/10.1177/0261927X09351676 (2010).
33. Gonzalez-Bailon, S. & Paltoglou, G. Signals of public opinion in online communication: a comparison of methods and data sources. Ann. Am. Acad. Pol. Soc. Sci. 659, 95-107, https://doi.org/10.1177/0002716215569192 (2015).
34. Ceron, A., Curini, L. & Iacus, S. M. Using sentiment analysis to monitor electoral campaigns: method matters-evidence from the united states and italy. Soc. Sci. Comput. Rev. 33, 3-20, https://doi.org/10.1177/0894439314521983 (2015).
35. Beauchamp, N. Predicting and Interpolating state-level polls using Twitter textual data. Am. J. Pol. Sci. 00, 1-14, https://doi.org/10.1111/ajps.12274 (2016).
36. Sylwester, K. & Purver, M. Twitter language use reflects psychological differences between democrats and republicans. PLoS One 10, 1-18, https://doi.org/10.1371/journal.pone.0137422 (2015).
37. Ceron, A., Curini, L. & Iacus, S. M. ISA: A fast, scalable and accurate algorithm for sentiment analysis of social media content. Inf. Sci. (Ny). 367-368, 105-124, https://doi.org/10.1016/j.ins.2016.05.052 (2016).
38. Ceron, A., Curini, L. & Iacus, S. Politics and Big Data: Nowcasting and Forecasting Elections with Social Media (Taylor & Francis, 2017).
39. Hopkins, D. J. & King, G. A method of automated nonparametric content analysis for social science. Am. J. Pol. Sci. 54, 229-247, https://doi.org/10.1111/j.1540-5907.2009.00428.x (2010).
40. New York Times. New York Times National Polling Average http://www.nytimes.com/interactive/2016/us/elections/polls.html [Online; accessed 24-April-2017] (2016).
41. Ceron, A., Curini, L. & Iacus, S. Politics and Big Data: Nowcasting and Forecasting Elections with Social Media, chap. 3 (Taylor & Francis, 2017).
42. Pei, S., Muchnik, L., Andrade, J. S. Jr., Zheng, Z. & Makse, H. A. Searching for superspreaders of information in real-world social media. Sci. Rep. 4, 5547, https://doi.org/10.1038/srep05547 (2014).
43. Bunde, A. & Havlin, S. Fractals and Disordered Systems (Springer Berlin Heidelberg, 2012).
44. Bollobás, B. Random Graphs. Cambridge Studies in Advanced Mathematics (Cambridge University Press, 2001).
45. Raghavan, U. N. & Albert, R. & Kumara, S. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys. 76, 1-11, https://doi.org/10.1103/PhysRevE.76.036106 (2007).
46. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008, https://doi.org/10.1088/1742-5468/2008/10/P10008 (2008).
47. Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Physical Review E 69, 026113, https://doi.org/10.1103/PhysRevE.69.026113 (2004).
48. Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A. & Bonneau, R. Tweeting from left to right. Psychol. Sci. 26, 1531-1542, https://doi.org/10.1177/0956797615594620 (2015).
49. Mills, T. Time Series Techniques for Economists (Cambridge University Press, 1991).
50. Seabold, S. & Perktold, J. Statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference (2010).
51. Amador Diaz Lopez, J. C., Collignon-Delmar, S., Benoit, K. & Matsuo, A. Predicting the Brexit Vote by Tracking and Classifying Public Opinion Using Twitter Data. Statistics, Politics and Policy 8, 85-104, https://doi.org/10.1515/spp-2017-0006 (2017).
52. Bohannon, J. The pulse of the people. Science 355, 470-472, https://doi.org/10.1126/science.355.6324.470 (2017).
53. Jasny, B. R. & Stone, R. Prediction and its limits. Science 355, 468-469, https://doi.org/10.1126/science.355.6324.468 (2017).
54. Wang, X., Wei, F., Liu, X., Zhou, M. & Zhang, M. Topic sentiment analysis in twitter. In Proc. 20th ACM Int. Conf. Inf. Knowl. Manag., 1031 (ACM Press, New York, New York, USA, https://doi.org/10.1145/2063576.2063726 (2011).
55. Pew Research Center. Assessing the representativeness of public opinion surveys http://www.people-press.org/2012/05/15/assessing-the-representativeness-of-public-opinion-surveys. [Online; accessed 5-October-2016] (2013).
56. Conover, M. D., Gonçalves, B., Flammini, A. & Menczer, F. Partisan asymmetries in online political activity. EPJ Data Sci. 1, 6, https://doi.org/10.1140/epjds6 (2012).
57. Pew Research Center. Social media update 2016 http://www.pewinternet.org/2016/11/11/social-media-update-2016/. [Online; accessed 7-March-2017] (2016).
58. Morstatter, F., Pfeffer, J., Liu, H. & Carley, K. Is the sample good enough? Comparing data from Twitter's streaming API with Twitter's firehose. In Proc. Seventh Int. AAAI Conf. Weblogs Soc. Media 400-408 https://doi.org/10.1007/978-3-319-05579-4-10 (2013).
59. Twitter Documentation. Twitter API authentication https://developer.twitter.com/en/docs/basics/authentication/guides/accesstokens [Online; accessed 26-February-2018] (2018).
60. Twitter Documentation. Tweet object https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object [Online; accessed 26-February-2018] (2018).
61. Varol, O., Ferrara, E., Davis, C. A., Menczer, F. & Flammini, A. Online human-bot interactions: detection, estimation, and characterization. In Proc. 11th Int. AAAI Conf. Weblogs Soc. Media 280-289 (2017).
62. Martinez-Romo, J. et al. Disentangling categorical relationships through a graph of co-occurrences. Phys. Rev. E 84, 1-8, https://doi.org/10.1103/PhysRevE.84.046108 (2011).
63. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning, vol. 1 of Springer Series in Statistics (Springer New York, New York, NY, 2009).
64. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825-2830 (2011).