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Found 71 papers, 12 papers with code
HIT&QMUL at SemEval-2022 Task 9: Label-Enclosed Generative Question Answering (LEG-QA)
As a result, the model can well generalise to soft constrained and other competence-based question answering problem.
MultiLexNorm: A Shared Task on Multilingual Lexical Normalization
This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation.
Team dina at SemEval-2022 Task 8: Pre-trained Language Models as Baselines for Semantic Similarity
This paper describes the participation of the team “dina” in the Multilingual News Similarity task at SemEval 2022.
Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation
Our experiments demonstrate the effectiveness of model components, not least the translation-augmented data as well as the adversarial learning component, to the improved performance of the model.
SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media
Stance detection, as the task of determining the viewpoint of a social media post towards a target as 'favor' or 'against', has been understudied in the challenging yet realistic scenario where there is limited labeled data for a certain target.
ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection
Swear words are a common proxy to collect datasets with cyberbullying incidents.
MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim Verification
Claim verification is an essential step in the automated fact-checking pipeline which assesses the veracity of a claim against a piece of evidence.
Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research
Focusing on multilingual misinformation, we present a comprehensive survey of existing multilingual claim detection research.
Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processing
The advancement of machine learning and symbolic approaches have underscored their strengths and weaknesses in Natural Language Processing (NLP).
Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges
This survey presents a systematic and comprehensive exploration of Cross-Lingual Transfer Learning (CLTL) techniques in offensive language detection in social media.
Faithful Knowledge Graph Explanations for Commonsense Reasoning
While fusing language models and knowledge graphs has become common in commonsense question answering research, enabling faithful chain-of-thought explanations in these models remains an open problem.
PANACEA: An Automated Misinformation Detection System on COVID-19
In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection.
Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes
Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes.
NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis
This paper describes the participation of our NUAA-QMUL-AIIT team in the Memotion 3 shared task on meme emotion analysis.
Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings
To address the cross-target stance detection in social media by leveraging the social nature of the task, we introduce CT-TN, a novel model that aggregates multimodal embeddings derived from both textual and network features of the data.
AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection
During training, the model associates annotators with their label choices given a piece of text; during evaluation, when label information is not available, the model predicts the aggregated label given by the participating annotators by utilising the learnt association.
Check-worthy Claim Detection across Topics for Automated Fact-checking
The AraCWA model enables boosting the performance for new topics by incorporating two components for few-shot learning and data augmentation.
SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media
The goal of sexism detection is to mitigate negative online content targeting certain gender groups of people.
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training
To mitigate the impact of the scarcity of labelled data on fact-checking systems, we focus on few-shot claim verification.
Session-based Cyberbullying Detection in Social Media: A Survey
In this survey paper, we define the Session-based Cyberbullying Detection framework that encapsulates the different steps and challenges of the problem.
Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification
In this paper, we introduce SEED, a novel vector-based method to few-shot claim veracity classification that aggregates pairwise semantic differences for claim-evidence pairs.
Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers
Therefore an ability to predict a model's ability to persist over time can help design models that can be effectively used over a longer period of time.
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims
The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims.
Hidden behind the obvious: misleading keywords and implicitly abusive language on social media
While social media offers freedom of self-expression, abusive language carry significant negative social impact.
Cyberbullying detection across social media platforms via platform-aware adversarial encoding
Despite the increasing interest in cyberbullying detection, existing efforts have largely been limited to experiments on a single platform and their generalisability across different social media platforms have received less attention.
Sexism Identification in Tweets and Gabs using Deep Neural Networks
These networks are used in conjunction with transfer learning in the form of Bidirectional Encoder Representations from Transformers (BERT) and DistilBERT models, along with data augmentation, to perform binary and multiclass sexism classification on the dataset of tweets and gabs from the sEXism Identification in Social neTworks (EXIST) task in IberLEF 2021.
Cross-lingual Hate Speech Detection using Transformer Models
Hate speech detection within a cross-lingual setting represents a paramount area of interest for all medium and large-scale online platforms.
Automated Fact-Checking: A Survey
As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years.
A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis
The corpus consists of spoken conversations, a subset of which are transcribed, as well as typed and written thoughts and associated extra-linguistic information such as pen strokes and keystrokes.
Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions
Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact.
The emojification of sentiment on social media: Collection and analysis of a longitudinal Twitter sentiment dataset
There is however a gap in the longitudinal study of how sentiment evolved in social media over the years.
Opinions are Made to be Changed: Temporally Adaptive Stance Classification
We propose a novel approach to mitigate this performance drop, which is based on temporal adaptation of the word embeddings used for training the stance classifier.
Weakly Supervised Cross-platform Teenager Detection with Adversarial BERT
Teenager detection is an important case of the age detection task in social media, which aims to detect teenage users to protect them from negative influences.
Cross-lingual Capsule Network for Hate Speech Detection in Social Media
Most hate speech detection research focuses on a single language, generally English, which limits their generalisability to other languages.
SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection
We propose the first Chinese sexism dataset -- Sina Weibo Sexism Review (SWSR) dataset --, as well as a large Chinese lexicon SexHateLex made of abusive and gender-related terms.
QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification
As a result, our team is the No.
Citizen Participation and Machine Learning for a Better Democracy
The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations.
Towards generalisable hate speech detection: a review on obstacles and solutions
Hate speech is one type of harmful online content which directly attacks or promotes hate towards a group or an individual member based on their actual or perceived aspects of identity, such as ethnicity, religion, and sexual orientation.
TF-CR: Weighting Embeddings for Text Classification
Text classification, as the task consisting in assigning categories to textual instances, is a very common task in information science.
Detection and Resolution of Rumors and Misinformation with NLP
Detecting and grounding false and misleading claims on the web has grown to form a substantial sub-field of NLP.
An Online Multilingual Hate speech Recognition System
After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model.
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis
This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis.
QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian
In this paper, we present the results and main findings of our system for the DIACR-ITA 2020 Task.
QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs
This paper presents our submission to the SardiStance 2020 shared task, describing the architecture used for Task A and Task B.
QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions
The purpose of this task is to determine the check-worthiness of tweets about COVID-19 to identify and prioritise tweets that need fact-checking.
Exploiting Class Labels to Boost Performance on Embedding-based Text Classification
Text classification is one of the most frequent tasks for processing textual data, facilitating among others research from large-scale datasets.
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.
Leveraging Aspect Phrase Embeddings for Cross-Domain Review Rating Prediction
Online review platforms are a popular way for users to post reviews by expressing their opinions towards a product or service, as well as they are valuable for other users and companies to find out the overall opinions of customers.
Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection
In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim.
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.
All-in-one: Multi-task Learning for Rumour Verification
We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification.
Mining Social Media for Newsgathering: A Review
Social media is becoming an increasingly important data source for learning about breaking news and for following the latest developments of ongoing news.
Early Detection of Social Media Hoaxes at Scale
Our dataset represents a realistic scenario with a real distribution of true, commemorative and false stories, which we release for further use as a benchmark in future research.
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.
TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter
We present a system for time sensitive, topic based summarisation of the sentiment around target entities and topics in collections of tweets.
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.
TDParse: Multi-target-specific sentiment recognition on Twitter
Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned.
Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity
Independence movements occur in territories whose citizens have conflicting national identities; users with opposing national identities will then support or oppose the sense of being part of an independent nation that differs from the officially recognised country.
Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media
In this paper we introduce a novel approach to rumour detection that learns from the sequential dynamics of reporting during breaking news in social media to detect rumours in new stories.
Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest.
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.
Using Fuzzy Logic to Leverage HTML Markup for Web Page Representation
The selection of a suitable document representation approach plays a crucial role in the performance of a document clustering task.
TweetMT: A Parallel Microblog Corpus
We introduce TweetMT, a parallel corpus of tweets in four language pairs that combine five languages (Spanish from/to Basque, Catalan, Galician and Portuguese), all of which have an official status in the Iberian Peninsula.
Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
In contrast to much previous work that has focused on location classification of tweets restricted to a specific country, here we undertake the task in a broader context by classifying global tweets at the country level, which is so far unexplored in a real-time scenario.
TweetNorm\_es: an annotated corpus for Spanish microtext normalization
It was created for Tweet-Norm, a tweet normalization workshop and shared task, and is the result of a joint annotation effort from different research groups.
Real-Time Classification of Twitter Trends
Social media users give rise to social trends as they share about common interests, which can be triggered by different reasons.
