Electoral%20Predictions%20with%20Twitter:%20a%20Machine-Learning%20approach

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Electoral Predictions with Twitter a
Machine-Learning approach

• M. Coletto1,3, C. Lucchese1, S. Orlando2, and R.
  Perego1
• 1 ISTI-CNR, Pisa
• 2 University Ca Foscari of Venice
• 3 IMT Institute for Advanced Studies, Lucca

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• INTRODUCTION

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

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• AGENDA

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

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• STATE-OF-THE-ART

• 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.

1 Bollen, J., Mao, H., Zeng, X. Twitter mood
predicts the stock market. Journal of Computa-
tional Science 2(1), 18 (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. 492499.
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. 599602. Springer
(2010) 4 OConnor, B., Balasubramanyan, R.,
Routledge, B.R., Smith, N.A. From tweets to
polls Linking text sentiment to public opinion
time series. ICWSM 11, 122129 (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, 178185 (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. 5360. Association for Computational
Linguistics, Stroudsburg, PA, USA (2012) 7
Caldarelli,G.,Chessa,A.,Pammolli,F.,Pompa,G.,Pulig
a,M.,Riccaboni,M.,Riotta,G.A multi-level
geographical study of italian political elections
from twitter data. PloS one 9(5), e95809 (2014)

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Volume-based
Content-based
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• DATA

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• BASELINE

• Tumasjan, A., Sprenger, T.O., Sandner, P.G.,
  Welpe, I.M. Predicting elections with twitter
  What 140 characters reveal about political
  sentiment. ICWSM 10, 178185 (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

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

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• METHODS

• Proposed classification methods
• UserShare
• ClassTweetCount
• ClassUserCount

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• METHODS 2

• 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

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• AGE BIAS

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• CONCLUSION

• New predictors
• Machine learning approach
• Age bias analysis
• LIMITATIONS AND FUTURE WORK
• Twitter bias
• Single dataset (European)
• Arbitrariness (window, keywords, ..)

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• THANK YOU
• QUESTIONS?