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Found 64 papers, 13 papers with code
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling
Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents.
Lying Blindly: Bypassing ChatGPT's Safeguards to Generate Hard-to-Detect Disinformation Claims at Scale
As Large Language Models (LLMs) become more proficient, their misuse in large-scale viral disinformation campaigns is a growing concern.
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks.
Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study
This paper analyses two hitherto unstudied sites sharing state-backed disinformation, Reliable Recent News (rrn. world) and WarOnFakes (waronfakes. com), which publish content in Arabic, Chinese, English, French, German, and Spanish.
Examining Temporal Bias in Abusive Language Detection
The use of abusive language online has become an increasingly pervasive problem that damages both individuals and society, with effects ranging from psychological harm right through to escalation to real-life violence and even death.
Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets
A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors.
Detecting Misinformation with LLM-Predicted Credibility Signals and Weak Supervision
Credibility signals represent a wide range of heuristics that are typically used by journalists and fact-checkers to assess the veracity of online content.
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient.
Finding Already Debunked Narratives via Multistage Retrieval: Enabling Cross-Lingual, Cross-Dataset and Zero-Shot Learning
The task of retrieving already debunked narratives aims to detect stories that have already been fact-checked.
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science
Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts.
A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation
This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets.
Examining Temporalities on Stance Detection towards COVID-19 Vaccination
It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccination on a large scale.
SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification
For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensembles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining.
It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits
New events emerge over time influencing the topics of rumors in social media.
VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter
Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination.
On the Impact of Temporal Concept Drift on Model Explanations
Explanation faithfulness of model predictions in natural language processing is typically evaluated on held-out data from the same temporal distribution as the training data (i. e. synchronous settings).
Classifying COVID-19 vaccine narratives
This paper addresses the need for monitoring and analysing vaccine narratives online by introducing a novel vaccine narrative classification task, which categorises COVID-19 vaccine claims into one of seven categories.
Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic
In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e. g. comment or question to the misinformation).
European Language Grid: A Joint Platform for the European Language Technology Community
Europe is a multilingual society, in which dozens of languages are spoken.
MP Twitter Engagement and Abuse Post-first COVID-19 Lockdown in the UK: White Paper
This work covers the period of June to December 2020 and analyses Twitter abuse in replies to UK MPs.
Multistage BiCross encoder for multilingual access to COVID-19 health information
The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic' of health information online.
Measuring What Counts: The case of Rumour Stance Classification
This paper specifically questions the evaluation metrics used in these shared tasks.
Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis
Therefore, identifying these comments is an important task for studying and preventing the proliferation of toxicity in social media.
Ranked #1 on
Hate Speech Detection
on ToLD-Br
Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide.
Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour
Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk.
Measuring the Impact of Readability Features in Fake News Detection
The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health.
Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows.
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality.
European Language Grid: An Overview
With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs).
Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis
The system features a rumour annotation service that allows journalists to easily provide feedback for a given social media post through a web-based interface.
The evolution of argumentation mining: From models to social media and emerging tools
In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media.
Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network
This paper describes the participation of team {``}bertha-von-suttner{''} in the SemEval2019 task 4 Hyperpartisan News Detection task.
SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour{'}s veracity.
RumourEval 2019: Determining Rumour Veracity and Support for Rumours
This is the proposal for RumourEval-2019, which will run in early 2019 as part of that year's SemEval event.
Can Rumour Stance Alone Predict Veracity?
Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity.
Helping Crisis Responders Find the Informative Needle in the Tweet Haystack
Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts.
Discourse-Aware Rumour Stance Classification in Social Media Using Sequential Classifiers
We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers.
Simple Open Stance Classification for Rumour Analysis
Stance classification determines the attitude, or stance, in a (typically short) text.
Automatic Summarization of Online Debates
In our work, we investigate two different clustering approaches for the generation of the summaries.
Gold Standard Online Debates Summaries and First Experiments Towards Automatic Summarization of Online Debate Data
In this paper, we collected and annotated debate data for an automatic summarization task.
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Media is full of false claims.
Detection and Resolution of Rumours in Social Media: A Survey
Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i. e. pieces of information that are unverified at the time of posting.
Generalisation in Named Entity Recognition: A Quantitative Analysis
Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire.
Broad Twitter Corpus: A Diverse Named Entity Recognition Resource
One of the main obstacles, hampering method development and comparative evaluation of named entity recognition in social media, is the lack of a sizeable, diverse, high quality annotated corpus, analogous to the CoNLL{'}2003 news dataset.
Using Gaussian Processes for Rumour Stance Classification in Social Media
Social media tend to be rife with rumours while new reports are released piecemeal during breaking news.
Stance Detection with Bidirectional Conditional Encoding
Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral".
Monolingual Social Media Datasets for Detecting Contradiction and Entailment
Entailment recognition approaches are useful for application domains such as information extraction, question answering or summarisation, for which evidence from multiple sentences needs to be combined.
Challenges of Evaluating Sentiment Analysis Tools on Social Media
This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems.
USFD: Twitter NER with Drift Compensation and Linked Data
This paper describes a pilot NER system for Twitter, comprising the USFD system entry to the W-NUT 2015 NER shared task.
Classifying Tweet Level Judgements of Rumours in Social Media
Social media is a rich source of rumours and corresponding community reactions.
Analysis of Named Entity Recognition and Linking for Tweets
Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area.
Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines
Crowdsourcing is an emerging collaborative approach that can be used for the acquisition of annotated corpora and a wide range of other linguistic resources.
