Please use this identifier to cite or link to this item:
|Detecting Persuasion Attempts on Social Networks: Unearthing the Potential of Loss Functions and Text Pre-Processing in Imbalanced Data Settings
|Natural Language Processing
|MDPI - Electronics
|Rúben Teimas and José Saias. 2023. "Detecting Persuasion Attempts on Social Networks: Unearthing the Potential of Loss Functions and Text Pre-Processing in Imbalanced Data Settings" Electronics 12, no. 21: 4447.
|The rise of social networks and the increasing amount of time people spend on them
have created a perfect place for the dissemination of false narratives, propaganda, and manipulated
content. In order to prevent the spread of disinformation, content moderation is needed. However,
manual moderation is unfeasible due to the large amount of daily posts. This paper studies the
impact of using different loss functions on a multi-label classification problem with an imbalanced
dataset, consisting of 20 persuasion techniques and only 950 samples, provided by SemEval’s 2021
Task 6. We used machine learning models, such as Naive Bayes and Decision Trees, and a custom
deep learning architecture, based on DistilBERT and Convolutional Layers. Overall, the machine
learning models achieved far worse results than the deep learning model, using Binary Cross Entropy,
which we considered our baseline deep learning model. To address the class imbalance problem, we
trained our model using different loss functions, such as Focal Loss and Asymmetric Loss. The latter
providing the best results, particularly for the least represented classes.
|Appears in Collections:
|INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
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