1,312 research outputs found
Time of your hate: The challenge of time in hate speech detection on social media
The availability of large annotated corpora from social media and the development of powerful classification approaches have contributed in an unprecedented way to tackle the challenge of monitoring users' opinions and sentiments in online social platforms across time. Such linguistic data are strongly affected by events and topic discourse, and this aspect is crucial when detecting phenomena such as hate speech, especially from a diachronic perspective. We address this challenge by focusing on a real case study: the "Contro l'odio" platform for monitoring hate speech against immigrants in the Italian Twittersphere. We explored the temporal robustness of a BERT model for Italian (AlBERTo), the current benchmark on non-diachronic detection settings. We tested different training strategies to evaluate how the classification performance is affected by adding more data temporally distant from the test set and hence potentially different in terms of topic and language use. Our analysis points out the limits that a supervised classification model encounters on data that are heavily influenced by events. Our results show how AlBERTo is highly sensitive to the temporal distance of the fine-tuning set. However, with an adequate time window, the performance increases, while requiring less annotated data than a traditional classifier
Killing me Softly: Creative and Cognitive Aspects of Implicitness in Abusive Language Online
[EN] Abusive language is becoming a problematic issue for our society. The spread of messages that reinforce social and cultural intolerance could have dangerous effects in victimsÂż life. State-of-the-art technologies are often effective on detecting explicit forms of abuse, leaving unidentified the utterances with very weak offensive language but a strong hurtful effect. Scholars have advanced theoretical and qualitative observations on specific indirect forms of abusive language that make it hard to be recognized automatically. In
this work, we propose a battery of statistical and computational analyses able to support these considerations, with a focus on creative and cognitive aspects of the implicitness, in texts coming from different sources such as social media and news. We experiment with transformers, multi-task learning technique, and a set of linguistic features to reveal the elements involved in the implicit and explicit manifestations of abuses, providing a solid basis for computational applications.Frenda, S.; Patti, V.; Rosso, P. (2022). Killing me Softly: Creative and Cognitive Aspects of Implicitness in Abusive Language Online. Natural Language Engineering. 1-22. https://doi.org/10.1017/S135132492200031612
Dynamics of online hate and misinformation
Online debates are often characterised by extreme polarisation and heated discussions among
users. The presence of hate speech online is becoming increasingly problematic, making necessary
the development of appropriate countermeasures. In this work, we perform hate speech detection
on a corpus of more than one million comments on YouTube videos through a machine learning
model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there
is no evidence of the presence of “pure haters”, meant as active users posting exclusively hateful
comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed
towards one of the two categories of video channels (questionable, reliable) are more prone to use
inappropriate, violent, or hateful language within their opponents’ community. Interestingly, users
loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find
that the overall toxicity of the discussion increases with its length, measured both in terms of the
number of comments and time. Our results show that, coherently with Godwin’s law, online debates
tend to degenerate towards increasingly toxic exchanges of views
EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020
Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)
HateBERT:Retraining BERT for Abusive Language Detection in English
In this paper, we introduce HateBERT, a re-trained BERT model for abusive
language detection in English. The model was trained on RAL-E, a large-scale
dataset of Reddit comments in English from communities banned for being
offensive, abusive, or hateful that we have collected and made available to the
public. We present the results of a detailed comparison between a general
pre-trained language model and the abuse-inclined version obtained by
retraining with posts from the banned communities on three English datasets for
offensive, abusive language and hate speech detection tasks. In all datasets,
HateBERT outperforms the corresponding general BERT model. We also discuss a
battery of experiments comparing the portability of the generic pre-trained
language model and its corresponding abusive language-inclined counterpart
across the datasets, indicating that portability is affected by compatibility
of the annotated phenomena
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