I was reminiscing last week about our previous MP. James Lunney was a Reform/Conservative MP and he was 'famous’ for saying dumb anti-science things. He was also a chiropractor... which tracks. He was the only politician on Twitter to block me. He didn't like his constituents asking him how his ancestors walked with T-Rex I guess. 🤷♂️
Here's the blast from the past:
Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance
Rachith Aiyappa, Shruthi Senthilmani, Jisun An, Haewoon Kwak, Yong-Yeol Ahn
https://arxiv.org/abs/2403.00236
Screenshotted this past tweet of mine from April 3rd in case some of the people who I removed are wondering why they're no longer following me and can no longer see my tweets on-site:
Users invented retweets.
They typed “RT” and then pasted in the text of a tweet they liked. The practice became so widespread that Twitter turned it into an official feature.
Quote-tweets were created by Twitter users, too: they typed a commentary on someone else’s tweet, then pasted in the URL of that tweet. Voila, the quote-tweet was born.
And users invented tweet-threading: the first Twitter threads were created by posting a tweet, then replying to your own tweet, then r…
Huh... I didn't realize that somewhere along the way X restricted access to public tweets/posts to logged-in users.
I noticed this when viewing X profiles from a browser where I was NOT logged in. Then I went to https://twitter.com/danyork - all it showed me was my one pinned tweet (amusingly…
Persistent Homology generalizations for Social Media Network Analysis
Isabela Rocha
https://arxiv.org/abs/2404.19257 https://arxiv.org/pdf/2404.19257
arXiv:2404.19257v1 Announce Type: new
Abstract: This study details an approach for the analysis of social media collected political data through the lens of Topological Data Analysis, with a specific focus on Persistent Homology and the political processes they represent by proposing a set of mathematical generalizations using Gaussian functions to define and analyze these Persistent Homology categories. Three distinct types of Persistent Homologies were recurrent across datasets that had been plotted through retweeting patterns and analyzed through the k-Nearest-Neighbor filtrations. As these Persistent Homologies continued to appear, they were then categorized and dubbed Nuclear, Bipolar, and Multipolar Constellations. Upon investigating the content of these plotted tweets, specific patterns of interaction and political information dissemination were identified, namely Political Personalism and Political Polarization. Through clustering and application of Gaussian density functions, I have mathematically characterized each category, encapsulating their distinctive topological features. The mathematical generalizations of Bipolar, Nuclear, and Multipolar Constellations developed in this study are designed to inspire other political science digital media researchers to utilize these categories as to identify Persistent Homology in datasets derived from various social media platforms, suggesting the broader hypothesis that such structures are bound to be present on political scraped data regardless of the social media it's derived from. This method aims to offer a new perspective in Network Analysis as it allows for an exploration of the underlying shape of the networks formed by retweeting patterns, enhancing the understanding of digital interactions within the sphere of Computational Social Sciences.
If anyone had ever wondered why I followed @sassycrass (RIP) over on Twitter* here’s just one reminder from back in 2022: Another Mask Incident
*That’s a Trick Question - I can’t imagine anyone wondering that.
Screenshotted this past tweet of mine from April 3rd in case some of the people who I removed are wondering why they're no longer following me and can no longer see my tweets on-site:
ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents
Hoang-Thang Ta, Abu Bakar Siddiqur Rahman, Lotfollah Najjar, Alexander Gelbukh
#SMM4H (Social Media Mining for Health) 2024 Workshop, explicitly targeting the classification challenges within tweet data. Task 3 is a multi-class classification task centered on tweets discussing the impact of outdoor environments on symptoms of social anxiety. Task 5 involves a binary classification task focusing on tweets reporting medical disorders in children. We applied transfer learning from pre-trained encoder-decoder models such as BART-base and T5-small to identify the labels of a set of given tweets. We also presented some data augmentation methods to see their impact on the model performance. Finally, the systems obtained the best F1 score of 0.627 in Task 3 and the best F1 score of 0.841 in Task 5.
Huh... I didn't realize that somewhere along the way X restricted access to public tweets/posts to logged-in users.
I noticed this when viewing X profiles from a browser where I was NOT logged in. Then I went to https://twitter.com/danyork - all it showed me was my one pinned tweet (amusingly…
LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data
Vijeta Deshpande, Minhwa Lee, Zonghai Yao, Zihao Zhang, Jason Brian Gibbons, Hong Yu
https://arxiv.org/abs/2402.13452