49 research outputs found
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 victims¿ 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
Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets
[EN] In the last years, the control of online user generated content is becoming a priority, because of the increase of online aggressiveness and hate speech legal cases. Considering the complexity and the importance of this issue, this paper presents an approach that combines the deep learning framework with linguistic features for the recognition of aggressiveness in Mexican tweets. This approach has been evaluated relying on
a collection of tweets released by the organizers of
the shared task about aggressiveness detection in the
context of the Ibereval 2018 evaluation campaign. The
use of a benchmark corpus allows to compare the
results with those obtained by Ibereval 2018 participant
systems. However, looking at the achieved results,
linguistic features seem not to help the deep learning
classification for this task.The work of Simona Frenda and Paolo Rosso was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).Frenda, S.; Banerjee, S.; Rosso, P.; Patti, V. (2020). Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets. Computación y Sistemas. 24(2):633-643. https://doi.org/10.13053/CyS-24-2-3398S63364324