Coletto, Lucchese, Orlando, Perego

Electoral Predictions with Twitter: a Machine-Learning approach

M. Coletto1,3, C. Lucchese1, S. Orlando2, and R. Perego1

1 ISTI-CNR, Pisa2 University Ca’ Foscari of Venice3 IMT Institute for Advanced Studies, Lucca

May 2015

Coletto, Lucchese, Orlando, Perego

In this work we study how Twitter can provide some interesting insights concerning the primary elections of an Italian political party.

INTRODUCTION

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• STATE-OF-THE-ART• DATA• BASELINE• METHODS• AGE BIAS• CONCLUSION

AGENDA

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Twitter for predictive tasks: from prediction of stock market [1] to movie sales [2], and pandemics detection [3].

Many articles propose quantitative approaches to predict the electoral results in different countries: US [4], Germany [5], Holland [6], Italy [7].

STATE-OF-THE-ART

[1] Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computa- tional Science 2(1), 1–8 (2011) [2] Asur, S., Huberman, B.A.: Predicting the future with social media. In: Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on. vol. 1, pp. 492–499. IEEE (2010) [3] Lampos, V., De Bie, T., Cristianini, N.: Flu detector-tracking epidemics on twitter. In: Ma- chine Learning and Knowledge Discovery in Databases, pp. 599–602. Springer (2010) [4] O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. ICWSM 11, 122–129 (2010) [5] Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM 10, 178–185 (2010) [6] Sang, E.T.K., Bos, J.: Predicting the 2011 dutch senate election results with twit- ter. In: Proceedings of the Workshop on Semantic Analysis in Social Media. pp. 53–60. Association for Computational Linguistics, Stroudsburg, PA, USA (2012) [7] Caldarelli,G.,Chessa,A.,Pammolli,F.,Pompa,G.,Puliga,M.,Riccaboni,M.,Riotta,G.:A multi-level geographical study of italian political elections from twitter data. PloS one 9(5), e95809 (2014) 26/05/15 4

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DATA

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Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM 10, 178–185 (2010)

TweetCount

DiGrazia, J., McKelvey, K., Bollen, J., Rojas, F.: More tweets, more votes: Social media as a quantitative indicator of political behavior. PloS one 8(11), e79449 (2013)

UserCount

BASELINE

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• EVALUATION:- MAE(mean absolute error)- RMSE (root-mean-square error)- MRM(mean rank match)

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Proposed classification methods-UserShare

-ClassTweetCount

-ClassUserCount

METHODS

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• Training correcting factors through ML– Per candidate– Learning weights to evaluate Twitter

user/ voters ratio– Metrics: UserShare, ClassTweetCount

• Content Analysis (100 most frequent hash-tags)– 1 feature per word– Sentiment Analysis per candidate

METHODS 2

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AGE BIAS

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• New predictors• Machine learning approach• Age bias analysis

LIMITATIONS AND FUTURE WORK• Twitter bias• Single dataset (European)• Arbitrariness (window, keywords, ..)

CONCLUSION

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THANK YOU

QUESTIONS?