Tootfinder

[Carl Sagan voice]
If you wish to make an apple pie from scratch, you must first stop reposting screenshots of tweets from fascist government agencies.

What is happening here? F-35s for fighting against drugs traffickers is very strange. An attack against Venezuela would be a good distraction for Trump from everything that is going wrong elsewhere.
https://www.twz.com/air/10-f-35s-deploying

Victory Monday Social Reacts: Jaguars Fans Positive Response to Overtime Victory over Las Vegas Raiders in Week 9 of the 2025 Season https://www.jaguars.com/news/k000505-victory…

Two updates to old posts, both from social media bits:
1. https://adrianroselli.com/2023/09/the-children-are-driving-the-bus.html#Update01 adds this mini-thread for Bootlicker.js:

The Children Are Driving the Bus
The Vercel CEO over on the hell-site tweets: v0.dev produces the kind of production-grade code that we'd want to ship in our own @vercel products. That was the bar we set for ourselves. At the moment it can output HTML with @tailwindcss and React w/ @shadcn UI. pic.twitter.com/hWLzpmyaG2 Guillermo Rauch…

So it was “retweet with comment”. https://techcrunch.com/2015/04/06/retweetception/ (April 2015)
Screenshot from

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/8]:
- Can social media provide early warning of retraction? Evidence from critical tweets identified by...
Er-Te Zheng, Hui-Zhen Fu, Mike Thelwall, Zhichao Fang

I migrated to #Bandcamp in early 2022 before #Spotify started serving #ICE as “free content.”
Collection 246
Wishlist 155

SenWave: A Fine-Grained Multi-Language Sentiment Analysis Dataset Sourced from COVID-19 Tweets
Qiang Yang, Xiuying Chen, Changsheng Ma, Rui Yin, Xin Gao, Xiangliang Zhang
https://arxiv.org/abs/2510.08214

SenWave: A Fine-Grained Multi-Language Sentiment Analysis Dataset Sourced from COVID-19 Tweets
The global impact of the COVID-19 pandemic has highlighted the need for a comprehensive understanding of public sentiment and reactions. Despite the availability of numerous public datasets on COVID-19, some reaching volumes of up to 100 billion data points, challenges persist regarding the availability of labeled data and the presence of coarse-grained or inappropriate sentiment labels. In this paper, we introduce SenWave, a novel fine-grained multi-language sentiment analysis dataset specific…

At least 2/3 of Americans aren’t daily coffee drinkers. ☕️
Wait, they are.
Well, at least hundreds of millions of American coffee drinkers don’t drink an average of 3 cups a day.
Wait, they do.
Folks, these are #TrumpTariffs, taking money from the American people.
via @…

Replaced article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new
[1/1]:
- Can social media provide early warning of retraction? Evidence from critical tweets identified by...
Er-Te Zheng, Hui-Zhen Fu, Mike Thelwall, Zhichao Fang

And then it became "quote tweet" in 2020.
Screenshot from https://www.iphonetricks.org/how-to-use-the-new-twitter-quote-tweets/

Just opened the twitter profile for a company from search results, not logged in, was confused they hadn't tweeted since 2021...
No no, the tweets are just not in chronological order. They weren't pinned, they were just random. wtaf.

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…

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Andrea Nasuto, Stefano Maria Iacus, Francisco Rowe, Devika Jain
https://arxiv.org/abs/2508.06435

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Large language models (LLMs) are transforming social-science research by enabling scalable, precise analysis. Their adaptability raises the question of whether knowledge acquired through fine-tuning in a few languages can transfer to unseen languages that only appeared during pre-training. To examine this, we fine-tune lightweight LLaMA 3.2-3B models on monolingual, bilingual, or multilingual data sets to classify immigration-related tweets from X/Twitter across 13 languages, a domain character…