195 research outputs found
Classifying Tweet Level Judgements of Rumours in Social Media
Social media is a rich source of rumours and corresponding community
reactions. Rumours reflect different characteristics, some shared and some
individual. We formulate the problem of classifying tweet level judgements of
rumours as a supervised learning task. Both supervised and unsupervised domain
adaptation are considered, in which tweets from a rumour are classified on the
basis of other annotated rumours. We demonstrate how multi-task learning helps
achieve good results on rumours from the 2011 England riots
Social Media and Information Overload: Survey Results
A UK-based online questionnaire investigating aspects of usage of
user-generated media (UGM), such as Facebook, LinkedIn and Twitter, attracted
587 participants. Results show a high degree of engagement with social
networking media such as Facebook, and a significant engagement with other
media such as professional media, microblogs and blogs. Participants who
experience information overload are those who engage less frequently with the
media, rather than those who have fewer posts to read. Professional users show
different behaviours to social users. Microbloggers complain of information
overload to the greatest extent. Two thirds of Twitter-users have felt that
they receive too many posts, and over half of Twitter-users have felt the need
for a tool to filter out the irrelevant posts. Generally speaking, participants
express satisfaction with the media, though a significant minority express a
range of concerns including information overload and privacy
Classifying COVID-19 vaccine narratives
Vaccine hesitancy is widespread, despite the government's information
campaigns and the efforts of the World Health Organisation (WHO). Categorising
the topics within vaccine-related narratives is crucial to understand the
concerns expressed in discussions and identify the specific issues that
contribute to vaccine hesitancy. 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. Following a data augmentation approach, we first
construct a novel dataset for this new classification task, focusing on the
minority classes. We also make use of fact-checker annotated data. The paper
also presents a neural vaccine narrative classifier that achieves an accuracy
of 84% under cross-validation. The classifier is publicly available for
researchers and journalists.Comment: In Proceedings of the 14th International Conference on Recent
Advances in Natural Language Processing, 202
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. Machine learning models have been developed to automatically detect
abusive language, but these models can suffer from temporal bias, the
phenomenon in which topics, language use or social norms change over time. This
study aims to investigate the nature and impact of temporal bias in abusive
language detection across various languages and explore mitigation methods. We
evaluate the performance of models on abusive data sets from different time
periods. Our results demonstrate that temporal bias is a significant challenge
for abusive language detection, with models trained on historical data showing
a significant drop in performance over time. We also present an extensive
linguistic analysis of these abusive data sets from a diachronic perspective,
aiming to explore the reasons for language evolution and performance decline.
This study sheds light on the pervasive issue of temporal bias in abusive
language detection across languages, offering crucial insights into language
evolution and temporal bias mitigation
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