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Articles by Fred Morstatter
The shrinking landscape of linguistic diversity in the age of large language models
Abstract Language is far more than a communication tool; it encodes a wealth of information about a person’s identity, psychological state and social context, providing valuable insights for diverse fields including psychology, marketing and healthcare.
The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance
[Submitted on 8 Jan 2024 ( v1 ), last revised 9 Jan 2024 (this version, v2)] Title:The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance Download a PDF of the paper titled The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance, by Abel Salinas and Fred Morstatter Download PDF HTML (experimental) Abstract:Large Language Models (LLMs) are regularly being used to label data...
Twitter’s tampered samples: Limitations of big data sampling in social media
Social networks are widely used as sources of data in computational social science studies, and so it is of particular importance to determine whether these datasets are bias-free. In EPJ Data Science, Jürgen Pfeffer, Katja Mayer and Fred Morstatter demonstrate how Twitter’s sampling mechanism is prone to manipulation that could influence how researchers, journalists, marketeers and policy analysts interpret their data.
Twitter’s tampered samples: Limitations of big data sampling in social media
Social networks are widely used as sources of data in computational social science studies, and so it is of particular importance to determine whether these datasets are bias-free. In EPJ Data Science, Jürgen Pfeffer, Katja Mayer and Fred Morstatter demonstrate how Twitter’s sampling mechanism is prone to manipulation that could influence how researchers, journalists, marketeers and policy analysts interpret their data.
Tampering with Twitter’s Sample API
Received: 12 December 2017 Accepted: 11 December 2018 Published: 19 December 2018 Abstract Social media data is widely analyzed in computational social science. Twitter, one of the largest social media platforms, is used for research, journalism, business, and government to analyze human behavior at scale. Twitter offers data via three different Application Programming Interfaces (APIs). One of which, Twitter’s Sample API, provides a freely available 1% and a costly 10% sample of all Tweets.
Measuring Topic Interpretability with Crowdsourcing
Topic modelling is an important statistical modelling technique to discover abstract topics in collection of documents. This article talks about a new measure for assessing the semantic properties of statistical topics and how to use it. Machine learning algorithms can help produce models that are capable of revealing summaries of the dataset. Topic modelling algorithms, which are often trained on text corpora containing billions of documents, can reveal “themes”, or “topics” about the dataset.
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