Social networks are huge continuous sources of information that can be used to analyze people's behavior and thoughts. Our goal is to extract such information and predict political inclinations of users. In particular, we investigate the importance of syntactic features of texts written by users when they post on social media. Our hypothesis is that people belonging to the same political party write in similar ways, thus they can be classified properly on the basis of the words that they use. We analyze tweets because Twitter is commonly used in Italy for discussing about politics; moreover, it provides an official API that can be easily exploited for data extraction. Many classifiers were applied to different kinds of features and NLP vectorization methods in order to obtain the best method capable of confirming our hypothesis. To evaluate their accuracy, a set of current Italian deputies with consistent activity in Twitter has been selected as ground truth, and we have then predicted their political party. Using the results of our analysis, we also got interesting insights into current Italian politics. Our study is described in detail in the paper published in the IEEE Big Data 2018 conference and linked at: DOI: 10.1109/BigData.2018.8622040

1. Content-based Classification of
Political Inclinations of Twitter Users
Marco Di Giovanni, Marco Brambilla, Stefano Ceri
Florian Daniel, Giorgia Ramponi
10 December 2018ABCSS2018 @ IEEE BigData 2018
Politecnico di Milano

2. Goal:
• Classify political inclinations of Twitter users using the content in their
tweets
• Do users write in a similar way based on their political inclination?
• Problem: we don’t have a groundtruth!
• Select users with known political inclination: Politicians

3. Italian Politics
Chamber of deputies:
630 total deputies

4. Twitter

5. Pipeline
188 Twitter accounts
30643 tweets collected

6. Best Pipeline
Result: politicians write in similar ways based on their political party

7. T-SNE
From about 7900
to 2 dimentions
Silhouette score: 0.01

8. Confusion matrix
How accounts are classified

9. Relevant words

10. Relevant words

11. - Mosque
- Immigrant
- Party
Topic detection (LSA)
- Citizen
- Video
- Appointment
Common words
between topics:
- Law
- Chamber
- Minister

12. Future work
• Use more advanced vectorization techniques such as word2vec
• Apply to account of italian non politicians
• Integrate with other techniques, such as Network analysis

13. Thank you