ABOUT ME
I am a Ph.D. candidate in Economics at Universitat Autònoma de Barcelona and Barcelona School of Economics (IDEA Graduate Program).
My main research interest is the study of social media. I apply machine learning and text processing techniques combined with econometric methods
to understand things such as online coordination for offline protest participation, echo-chambers formation and tolerance to political content on traditional media.
You can check out my cv here.
RESEARCH
"Online and Offline protest participation: An Empirical Analysis for the 2020 Black Lives Matter Movement"
The recent wave of world-wide protests that took place after George Floyd’s killing has sparked attention in the Black Lives Matter movement,
specially in terms of online activism. How does offline protesting behavior interact with the underlying online social networks?
In this work, I build a classification algorithm to identify individuals who physically participated in the BLM denmostrations across US.
Thanks to this unique dataset, I explore at a individual level
their full Twitter activity to better understand the role of influencial users, coordination patterns and speech evolution.
Through this analysis, I aim to examine assumptions regarding slacktivism, which involves engaging in online activism
with minimal effort, in comparison to more traditional forms of protesting.
By exploring how social media contributes to the development of traditional activism, we can gain a deeper understanding of the role of social media in protesting behavior.
"Breaking the Echo Chamber: Nonviolent Protest and Police Violence on Twitter"
On 1st of October 2017 Catalunya held an illegal referendum on Independence from Spain.
The vote led to a heavy-handed response by Spanish law enforcement.
Images and videos of police violence against non-violent voters led to sharp critizisim. Making use of a unique dataset that spans the universe of Twitter activity in Spain during that period,
we analyze the evolution of online polarization. We introduce a simple model to
control for the possible content exposure that users might had and then use tweet language to study the interactions between the spanish and catalan Twitter communities.
"The Impact of Political Campaigns on Demand for Partisan News"
How do people acquire political information during political campaigns?
Using a unique dataset that comprises both audience data and text content from Spanish TV news,
we estimate the demand for political information. We rely on Large Language Models to categorize the tone associated with each political party in each story of the day.
To address endogeneity concerns regarding the political leaning of the content offered,
we use input shocks that constrain channels' political news production asymmetrically.
While outlets strive to maintain their political stance, these shocks affect them differently,
depending on the day's random news composition. Our findings suggest that soft content increases
engagement with hard news, supporting a variety-seeking mechanism. Moreover, political campaigns trigger polarized news demand,
with right-leaning viewers demanding more favorable content on their own party and more negative coverage of opposing parties.
TEACHING
- Teaching Assistant:
- Introduction to Text Mining and Natural Language Processing -- Data Science for Decision Making Program, BSE
- Econometrics I -- IDEA Master Programme, UAB
- Microeconometrics I -- IDEA Master Programme, UAB
- Master Thesis -- EPP Master Programme, BSE
- Others:
- Brush up- Introduction to Python Programming -- Data Science for Decision Making Program, BSE
CONTACT
Email: luisigmenendez@gmail.com
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