175 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
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
Examining Temporalities on Stance Detection Towards COVID-19 Vaccination
Previous studies have highlighted the importance of vaccination as an
effective strategy to control the transmission of the COVID-19 virus. It is
crucial for policymakers to have a comprehensive understanding of the public's
stance towards vaccination on a large scale. However, attitudes towards
COVID-19 vaccination, such as pro-vaccine or vaccine hesitancy, have evolved
over time on social media. Thus, it is necessary to account for possible
temporal shifts when analysing these stances. This study aims to examine the
impact of temporal concept drift on stance detection towards COVID-19
vaccination on Twitter. To this end, we evaluate a range of transformer-based
models using chronological and random splits of social media data. Our findings
demonstrate significant discrepancies in model performance when comparing
random and chronological splits across all monolingual and multilingual
datasets. Chronological splits significantly reduce the accuracy of stance
classification. Therefore, real-world stance detection approaches need to be
further refined to incorporate temporal factors as a key consideration
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