Search Results for author: Matti Wiegmann

Found 6 papers, 3 papers with code

Analyzing Persuasion Strategies of Debaters on Social Media

no code implementations COLING 2022 Matti Wiegmann, Khalid Al Khatib, Vishal Khanna, Benno Stein

Existing studies on the analysis of persuasion in online discussions focus on investigating the effectiveness of comments in discussions and ignore the analysis of the effectiveness of debaters over multiple discussions.

Persuasion Strategies Text Generation

Language Models as Context-sensitive Word Search Engines

1 code implementation In2Writing (ACL) 2022 Matti Wiegmann, Michael Völske, Benno Stein, Martin Potthast

Context-sensitive word search engines are writing assistants that support word choice, phrasing, and idiomatic language use by indexing large-scale n-gram collections and implementing a wildcard search.

Language Modelling

If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level

1 code implementation15 Apr 2024 Matti Wiegmann, Jennifer Rakete, Magdalena Wolska, Benno Stein, Martin Potthast

Trigger warnings are labels that preface documents with sensitive content if this content could be perceived as harmful by certain groups of readers.

Celebrity Profiling

1 code implementation ACL 2019 Matti Wiegmann, Benno Stein, Martin Potthast

Celebrities are among the most prolific users of social media, promoting their personas and rallying followers.

Gender Prediction Occupation prediction +1

Crowdsourcing a Large Corpus of Clickbait on Twitter

no code implementations COLING 2018 Martin Potthast, Tim Gollub, Kristof Komlossy, Sebastian Schuster, Matti Wiegmann, Garces Fern, Erika Patricia ez, Matthias Hagen, Benno Stein

To address the urging task of clickbait detection, we constructed a new corpus of 38, 517 annotated Twitter tweets, the Webis Clickbait Corpus 2017.

Clickbait Detection

Heuristic Feature Selection for Clickbait Detection

no code implementations4 Feb 2018 Matti Wiegmann, Michael Völske, Benno Stein, Matthias Hagen, Martin Potthast

We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017.

Clickbait Detection Feature Engineering +2

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