27 citations · 128 across the 13 of their papers we have counts for
14 papers
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…
Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter
Daniel Loureiro, Kiamehr Rezaee, Talayeh Riahi +4
This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models.…
Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts
Asahi Ushio, Leonardo Neves, Vitor Silva +2
Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-format…
Reciprocity, Homophily, and Social Network Effects in Pictorial Communication: A Case Study of Bitmoji Stickers
Julie Jiang, Ron Dotsch, Mireia Triguero Roura +4
Pictorial emojis and stickers are commonly used in online social networking to facilitate and aid communications. We delve into the use of Bitmoji stickers, a highly expressive for…
SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
Jiaxin Pei, Vítor Silva, Maarten Bos +4
We propose MINT, a new Multilingual INTimacy analysis dataset covering 13,372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese…
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…
TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media
Daniel Loureiro, Aminette D'Souza, Areej Nasser Muhajab +6
Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it chall…
Geolocated Social Media Posts are Happier: Understanding the Characteristics of Check-in Posts on Twitter
Julie Jiang, Jesse Thomason, Francesco Barbieri +1
The increasing prevalence of location-sharing features on social media has enabled researchers to ground computational social science research using geolocated data, affording oppo…
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…
TimeLMs: Diachronic Language Models from Twitter
Daniel Loureiro, Francesco Barbieri, Leonardo Neves +2
Despite its importance, the time variable has been largely neglected in the NLP and language model literature. In this paper, we present TimeLMs, a set of language models specializ…
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
Francesco Barbieri, Luis Espinosa Anke, Jose Camacho-Collados
Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focu…
The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks
Brihi Joshi, Neil Shah, Francesco Barbieri +1
Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis…
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification
Francesco Barbieri, Jose Camacho-Collados, Leonardo Neves +1
The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sent…
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning
Xisen Jin, Francesco Barbieri, Brendan Kennedy +3
Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous w…