Tootfinder

When I find it discouraging that young twenty-somethings often don’t seem to care about these things, it’s good for me to remember that I care a lot now, but I really don’t even remember these giant protests from when I was a young twenty-something.
I do remember talking with people and saying things like “going to war without actual evidence—just GWB saying stuff—is stupid” and being shot down by older folks.
But I really don’t remember most of the political action.

Dare Obasanjo (@carnage4life@mas.to)
Attached: 1 image Gen Z is about to experience the futility of protests that Gen X experienced 22 years ago. The way to create change is voting. Protesting is just slightly above writing mean tweets in influencing U.S. government policy. Slightly.

Online Anti-sexist Speech: Identifying Resistance to Gender Bias in Political Discourse
Aditi Dutta, Susan Banducci
https://arxiv.org/abs/2508.11434 https://

Online Anti-sexist Speech: Identifying Resistance to Gender Bias in Political Discourse
Anti-sexist speech, i.e., public expressions that challenge or resist gendered abuse and sexism, plays a vital role in shaping democratic debate online. Yet automated content moderation systems, increasingly powered by large language models (LLMs), may struggle to distinguish such resistance from the sexism it opposes. This study examines how five LLMs classify sexist, anti-sexist, and neutral political tweets from the UK, focusing on high-salience trigger events involving female Members of Par…

Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading
Yueyi Wang, Qiyao Wei
https://arxiv.org/abs/2508.07408

Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading
In this study, we wish to showcase the unique utility of large language models (LLMs) in financial semantic annotation and alpha signal discovery. Leveraging a corpus of company-related tweets, we use an LLM to automatically assign multi-label event categories to high-sentiment-intensity tweets. We align these labeled sentiment signals with forward returns over 1-to-7-day horizons to evaluate their statistical efficacy and market tradability. Our experiments reveal that certain event labels con…

Retweets, Receipts, and Resistance: Discourse, Sentiment, and Credibility in Public Health Crisis Twitter
Tawfiq Ammari, Anna Gutowska, Jacob Ziff, Casey Randazzo, Harihan Subramonyam
https://arxiv.org/abs/2505.22032

Retweets, Receipts, and Resistance: Discourse, Sentiment, and Credibility in Public Health Crisis Twitter
As the COVID-19 pandemic evolved, the Centers for Disease Control and Prevention (CDC) used Twitter to disseminate safety guidance and updates, reaching millions of users. This study analyzes two years of tweets from, to, and about the CDC using a mixed methods approach to examine discourse characteristics, credibility, and user engagement. We found that the CDCs communication remained largely one directional and did not foster reciprocal interaction, while discussions around COVID19 were deepl…

Central Bank Communication with Public: Bank of England and Twitter (X)
Fatih Kansoy, Joel Mundy
https://arxiv.org/abs/2506.02559 https://

Central Bank Communication with Public: Bank of England and Twitter (X)
Central banks increasingly use social media to communicate beyond financial markets, yet evidence on public engagement effectiveness remains limited. Despite 113 central banks joining Twitter between 2008 and 2018, we lack understanding of what drives audience interaction with their content. To examine engagement determinants, we analyzed 3.13 million tweets mentioning the Bank of England from 2007 to 2022, including 9,810 official posts. We investigate posting patterns, measure engagement elas…