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Measuring phenomena on Twitter Ongoing work

Opinion mining in social networks

Corrado Monti Universit` a degli Studi di Milano

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 1

Measuring phenomena on Twitter Ongoing work

Measuring phenomena on Twitter

◮ Sentiment analysis and textual classification can extract

information from the huge amount of Twitter messages

◮ Links with real-world indicators were discovered1 ◮ Ok, but can we predict elections with Twitter? ◮ (Quite obviously) not really.2

1O’Connor et al., From tweets to polls: Linking text sentiment to public

• pinion time series, ICWSM 11 (2010): 122-129.

2Chung and Mustafaraj, Can collective sentiment expressed on twitter

predict political elections? In 25th AAAI Conf. on AI, 2011.

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 2

Measuring phenomena on Twitter Ongoing work

◮ For which phenomena is this possible? ◮ Apparently economic trust is one of them3 ◮ Can political disaffection in Italy be measured through

massive tweet classification?

◮ It is a relevant phenomenon ◮ Lot of interest, academic (sociology) and not 3Bollen, Mao, Pepe, Modeling public mood and emotion: Twitter sentiment

and socio-economic phenomena, ICWSM 2011

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 3

Measuring phenomena on Twitter Ongoing work

Text classification

◮ “political disaffection” → political topic, negative sentiment,

presence of some keywords

◮ We had a training dataset of 28′340 labelled tweets ◮ We developed ad-hoc re-usable classification techniques

◮ We built robust classifiers, thanks to ontologies from DBpedia Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 4

Measuring phenomena on Twitter Ongoing work

Experimental comparison

Surveys

◮ Accepted way to measure

collective sentiment

◮ We got fraction of italians

that say they would not vote for any party

◮ One every ∼ 10 days in

April-October 2012

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 5

Measuring phenomena on Twitter Ongoing work

Experimental comparison

Surveys

◮ Accepted way to measure

collective sentiment

◮ We got fraction of italians

that say they would not vote for any party

◮ One every ∼ 10 days in

April-October 2012

Tweet sample

◮ 35′882′423 tweet ◮ For each survey, we compute

the ratio of disaffected tweet volume over political tweet volume from ∆ = 14 days before

Giugno 2012

L M M G V S D

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 5

Measuring phenomena on Twitter Ongoing work

Results

Pearson correlation index for ∆ = 14 days → ρ = 0.7860

• Apr 15

May 01 May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01 Sep 15 Oct 01 0.00 0.05 0.10 0.15 0.20 0.25 0.004 0.005 0.006 0.007 0.008 0.009 0.01 0.011

Time Inefficacy indicator Twitter disaffection ratio Twitter disaffection ratio Inefficacy indicator Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 6

Measuring phenomena on Twitter Ongoing work

Interpretation

◮ Data seem to indicate a good correlation between disaffected

tweet and diffusion of the phenomena in society

◮ This does not mean that Twitter is a representative sample! ◮ We can guess that the quantity of discussion about this

pheonomenon is connected with how much it will spread

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 7

Measuring phenomena on Twitter Ongoing work

We found peak causes from newspaper titles through text mining

Apr 01 Apr 15 May 01 May 15 Jun 01 Jun 15 Jul 01 Jul 15 Aug 01 Aug 15 Sep 01 Sep 15 Oct 01 0.000 0.005 0.010 0.015 0.020 0. 0.025 0.05 0.075 0.1 0.125 0.15 0.175 0.2

Time Twitter disaffection ratio Inefficacy indicator Twitter disaffection ratio Inefficacy indicator

Rapporto di tweet Sondaggi (Ineffjcacia) Tempo S

• n

d a g g i S

• n

d a g g i T w e e t

(Teorie del complotto su stragismo di Stato)

Scandalo Lega Amministrative Attentato di Brindisi Scandalo Fiorito

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 8

Measuring phenomena on Twitter Ongoing work

Ongoing work

◮ I plan to use these kind of data to better understand network

centrality measures

◮ We are developing a social network model where every node is

represented as a set of features

◮ Features can be also be opinions!

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 9

Measuring phenomena on Twitter Ongoing work

◮ In this model, every node has a priori ability to transmit

feature

◮ We are more or less able to reconstruct the value of this

ability through Gibbs Sampling

200 400 600 800 1000 node 0.1 1 10 R Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 10

Measuring phenomena on Twitter Ongoing work

Credits and References

◮ Corrado Monti, Matteo Zignani, Alessandro Rozza, Adam

Arvidsson, Giovanni Zappella, and Elanor Colleoni. Modelling political disaffection from twitter data, Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, p. 3. ACM, 2013.

◮ My supervisors are Paolo Boldi and Sebastiano Vigna ◮ Ongoing work with Irene Crimaldi (IMT Lucca)

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 11

Measuring phenomena on Twitter Ongoing work

Thanks!

email: corrado.monti@unimi.it

Corrado Monti Universit` a degli Studi di Milano Opinion mining in social networks 12