European Symposium Series on Societal Challenges in Computational Social Science

1. Gender inequalities in political participation:
a study of the 2017 UK general elections
on Twitter
Esther Gonzalez Caicedo¹ Matteo Manca² David Laniado²
¹ Universitat Pompeu Fabra, Barcelona, Spain
² Eurecat, Technology centre of Catalonia - Digital Humanities, Barcelona, Spain
Complex Networks - November 17th, 2017 London

2. Gender gap in political participation
● Strong evidence of gender gap in political decision
making positions
○ What about political participation more broadly?
● Literature suggests women in general participate less in
political activities and debates [Bode, 2017]
○ The higher the visibility of the actions, the stronger
the gap
● Women are more active on many social network sites
○ but what about online political debate?

3. Gender gap in political participation
● Gender inequalities in different kinds of activities for
influencing political decision-making

4. Research questions
● Is there a gender difference in online political
engagement?
● Is there a gender difference in the influence on political
online debate?
● Are there different patterns among supporters of different
parties?

5. Scenario: the UK election of 2017

6. Scenario: the UK election of 2017
Opinion polls evolution

7. Scenario: the UK election of 2017

8. Dataset
● Tweets related to the 2017 UK general elections
● Posted between May 4th and June 9th, 2017
● All tweets mentioning or containing:
○ party names or acronyms;
○ names of candidates for Prime Minister;
○ broadly used keywords associated to the elections:
generalelection2017, generalelection17, ge2017, ge17
● Overall dataset: 4.5 million tweets

9. ● Retweets as a proxy for political endorsement [Conover 2011]
● Network of retweets with threshold 3
○ A→B: if A retweeted at least three tweets by B
Retweet network
● Louvain algorithm to detect communities (clusters) of users densely
connected to each other
○ Clusters in retweet network to represent political parties [Aragón
2016]
● Pagerank to measure relevance in the network

10. Gender detection
● Combined results from two established tools:
○ Gender detector, Sexmachine
○ Based on baby names from different countries’ census data and other
available sources
○ Accuracy over 97% for both genders, according to previous studies
[Karimi 2016, Knowles 2016]
● Procedure:
○ Extract first name from Twitter handle
○ take results where the tools indicate the same gender, or one
indicates no clear gender;
➡ Overall coverage: ~ 67% labelled users
Unlabelled users correspond in part to account with no gender
(i.e. parties, organizations, media...)

11. Results: gender differences
Which are men and women? (guess it!)
Size of each word is proportional to the difference in frequency with
respect to the overall dataset

12. Results: gender differences
Which are men and women? (guess it!)
Size of each word is proportional to the difference in frequency with
respect to the overall dataset
women
men ➡

13. Results
● After election surveys, broken by gender
○ Confirm higher support to Labour from women

14. Gender composition of (party) clusters
● 9 main clusters, mostly related to political parties:
○ percentage of women around 40% in most clusters
○ lowest proportion in the conservatives party (25%)
● Do women have lower pagerank?
○ Top 10, 100 and 1000 users for each cluster, according to pagerank
○ Mann Whitney U-test confirms the hypothesis for most clusters
Significance level of the Mann Whitney U-test: P-values < 0.05 (*), < 0.01 (**), < 0.001 (***)

15. ● Lower participation of women in online political campaign
○ To be compared with debate on different topics
● Lower influence of women in the online political debate
○ Lower centrality of women in the network and in most party clusters
● Online engagement patterns in line with post-electoral
surveys
○ Greater support of men to the Conservative party
Conclusions

16. ● Evaluate robustness of the gender detection method
○ Apply our method to already annotated Twitter datasets
● Look at different kinds of interactions (replies, mentions..)
○ Are there gender difference in the preference for different kinds of
interactions (e.g., are women less likely to engage in discussions
replying to other messages)?
● Study interaction within clusters and interactions that cross
ideological barriers
○ Which gender has higher tendency to create ideological echo
chambers?
Future work

17. ● Investigate gender homophily of (different kinds of)
interactions:
○ Are men more likely to interact with other men, and/or women with
other women?
○ Is homophily stronger in retweets, replies or mentions?
○ Is it stronger or weaker across ideological borders (inter-cluster
interactions)?
● Study of language style and emotions as in [Iosub, 2012]
○ Apply our method to already annotated Twitter datasets
Future work

18. ● Aragón, P., Kappler, K. E., Kaltenbrunner, A., Laniado, D., and Volkovich, Y. (2013). Communication
dynamics in twitter during political campaigns: The case of the 2011 spanish national election.
Policy & Internet, 5(2):183–206.
● Aragón, P., Volkovich, Y., Laniado, D., & Kaltenbrunner, A. (2016). When a Movement Becomes a
Party: Computational Assessment of New Forms of Political Organization in Social Media. In
ICWSM (pp. 12-21).
● Bode, L. (2017). Closing the gap: Gender parity in political engagement on social media.
Information, Communication & Society, 20(4), 587-603.
● Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of
communities in large networks. Journal of Statistical Mechanics: Theory and Experiment,
2008(10):P10008.
● Childs, S. (2004). A British gender gap? Gender and political participation. The political quarterly,
75(4), 422-424.
● Conover, M., Ratkiewicz, J., Francisco, M. R., Gonçalves, B., Menczer, F., & Flammini, A. (2011).
Political polarization on twitter. ICWSM, 133, 89-96.
References

19. ● Karimi, F., Wagner, C., Lemmerich, F., Jadidi, M., & Strohmaier, M. (2016, April). Inferring gender
from names on the web: A comparative evaluation of gender detection methods. In Proceedings
of the 25th International Conference Companion on World Wide Web (pp. 53-54). International
World Wide Web Conferences Steering Committee.
● Knowles, R., Carroll, J., & Dredze, M. (2016). Demographer: Extremely simple name
demographics. NLP+ CSS 2016, 108.
● Iosub, D., Laniado, D., Castillo, C., Morell, M. F., & Kaltenbrunner, A. (2014). Emotions under
discussion: Gender, status and communication in online collaboration. PloS one, 9(8), e104880.
● Lietz, H., Wagner, C., Bleier, A., & Strohmaier, M. (2014). When Politicians Talk: Assessing Online
Conversational Practices of Political Parties on Twitter.
● Palla, G., Derényi, I., Farkas, I., and Vicsek, T. (2005). Uncovering the overlapping community
structure of complex networks in nature and society. Nature, 435(7043):814– 818.
References

20. Gender inequalities in political participation:
a study of the 2017 UK general elections
on Twitter
Thanks!!
Complex Networks - November 17th, 2017 London
matteo.manca@eurecat.org