79 citations · 119 across the 12 of their papers we have counts for
12 papers
Enhancing Item Tokenization for Generative Recommendation through Self-Improvement
Runjin Chen, Mingxuan Ju, Ngoc Bui +7
Generative recommendation systems, driven by large language models (LLMs), present an innovative approach to predicting user preferences by modeling items as token sequences and ge…
Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation
Joanne Boisson, Zara Siddique, Hsuvas Borkakoty +3
Extracting metaphors and analogies from free text requires high-level reasoning abilities such as abstraction and language understanding. Our study focuses on the extraction of the…
Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation
Dimosthenis Antypas, Indira Sen, Carla Perez-Almendros +2
The detection of sensitive content in large datasets is crucial for ensuring that shared and analysed data is free from harmful material. However, current moderation tools, such as…
Multilingual Topic Classification in X: Dataset and Analysis
Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri +1
In the dynamic realm of social media, diverse topics are discussed daily, transcending linguistic boundaries. However, the complexities of understanding and categorising this conte…
BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages
Junho Myung, Nayeon Lee, Yi Zhou +19
Large language models (LLMs) often lack culture-specific knowledge of daily life, especially across diverse regions and non-English languages. Existing benchmarks for evaluating LL…
Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit
Dimosthenis Antypas, Christian Arnold, Nedjma Ousidhoum +2
Political debates on social media often escalate quickly, leading to increased engagement as well as more emotional and polarised exchanges. Trigger points (Mau, Lux, and Westheuse…
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research
Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri +5
Despite its relevance, the maturity of NLP for social media pales in comparison with general-purpose models, metrics and benchmarks. This fragmented landscape makes it hard for the…
Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation
Dimosthenis Antypas, Jose Camacho-Collados
The automatic detection of hate speech online is an active research area in NLP. Most of the studies to date are based on social media datasets that contribute to the creation of h…
Twitter Topic Classification
Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados +3
Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A…
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi +11
In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic foc…
Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication
Dimosthenis Antypas, Alun Preece, Jose Camacho-Collados
Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to fo…
Deriving Disinformation Insights from Geolocalized Twitter Callouts
David Tuxworth, Dimosthenis Antypas, Luis Espinosa-Anke +3
This paper demonstrates a two-stage method for deriving insights from social media data relating to disinformation by applying a combination of geospatial classification and embedd…