754 research outputs found
A Framework for Sexism Detection on Social Media via ByT5 and TabNet
Hateful and offensive content on social media platforms particularly content directed towards a specific gender is a great impediment towards equality, diversity and inclusion. Social media platforms are facing increasing pressure to work towards regulation of such content; and this has directed researchers in text mining to work towards hate speech identification algorithms. One such attempt is sexism detection for which mostly transformer-based text methods have been proposed. We propose a combination of byte-level model ByT5 with tabular modeling via TabNet that has at its core an ability to take into account platform and language aspects of the challenging task of sexism detection. Despite not performing well in the sexism detection task for IberLEF our approach shows promise for future research in the area
CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions
The widespread popularity of social media has led to an increase in hateful,
abusive, and sexist language, motivating methods for the automatic detection of
such phenomena. The goal of the SemEval shared task \textit{Towards Explainable
Detection of Online Sexism} (EDOS 2023) is to detect sexism in English social
media posts (subtask A), and to categorize such posts into four coarse-grained
sexism categories (subtask B), and eleven fine-grained subcategories (subtask
C). In this paper, we present our submitted systems for all three subtasks,
based on a multi-task model that has been fine-tuned on a range of related
tasks and datasets before being fine-tuned on the specific EDOS subtasks. We
implement multi-task learning by formulating each task as binary pairwise text
classification, where the dataset and label descriptions are given along with
the input text. The results show clear improvements over a fine-tuned
DeBERTa-V3 serving as a baseline leading to -scores of 85.9\% in subtask A
(rank 13/84), 64.8\% in subtask B (rank 19/69), and 44.9\% in subtask C
(26/63).Comment: 11 pages, 4 figures, Accepted at The 17th International Workshop on
Semantic Evaluation, ACL 202
Multimodal and Explainable Internet Meme Classification
Warning: this paper contains content that may be offensive or upsetting. In
the current context where online platforms have been effectively weaponized in
a variety of geo-political events and social issues, Internet memes make fair
content moderation at scale even more difficult. Existing work on meme
classification and tracking has focused on black-box methods that do not
explicitly consider the semantics of the memes or the context of their
creation. In this paper, we pursue a modular and explainable architecture for
Internet meme understanding. We design and implement multimodal classification
methods that perform example- and prototype-based reasoning over training
cases, while leveraging both textual and visual SOTA models to represent the
individual cases. We study the relevance of our modular and explainable models
in detecting harmful memes on two existing tasks: Hate Speech Detection and
Misogyny Classification. We compare the performance between example- and
prototype-based methods, and between text, vision, and multimodal models,
across different categories of harmfulness (e.g., stereotype and
objectification). We devise a user-friendly interface that facilitates the
comparative analysis of examples retrieved by all of our models for any given
meme, informing the community about the strengths and limitations of these
explainable methods
A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions
In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content. Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media
Rationale-Guided Few-Shot Classification to Detect Abusive Language
Abusive language is a concerning problem in online social media. Past
research on detecting abusive language covers different platforms, languages,
demographies, etc. However, models trained using these datasets do not perform
well in cross-domain evaluation settings. To overcome this, a common strategy
is to use a few samples from the target domain to train models to get better
performance in that domain (cross-domain few-shot training). However, this
might cause the models to overfit the artefacts of those samples. A compelling
solution could be to guide the models toward rationales, i.e., spans of text
that justify the text's label. This method has been found to improve model
performance in the in-domain setting across various NLP tasks. In this paper,
we propose RGFS (Rationale-Guided Few-Shot Classification) for abusive language
detection. We first build a multitask learning setup to jointly learn
rationales, targets, and labels, and find a significant improvement of 6% macro
F1 on the rationale detection task over training solely rationale classifiers.
We introduce two rationale-integrated BERT-based architectures (the RGFS
models) and evaluate our systems over five different abusive language datasets,
finding that in the few-shot classification setting, RGFS-based models
outperform baseline models by about 7% in macro F1 scores and perform
competitively to models finetuned on other source domains. Furthermore,
RGFS-based models outperform LIME/SHAP-based approaches in terms of
plausibility and are close in performance in terms of faithfulness.Comment: 11 pages, 14 tables, 3 figures, The code repository is
https://github.com/punyajoy/RGFS_ECA
SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
To counter online abuse and misinformation, social media platforms have been
establishing content moderation guidelines and employing various moderation
policies. The goal of this paper is to study these community guidelines and
moderation practices, as well as the relevant research publications to identify
the research gaps, differences in moderation techniques, and challenges that
should be tackled by the social media platforms and the research community at
large. In this regard, we study and analyze in the US jurisdiction the fourteen
most popular social media content moderation guidelines and practices, and
consolidate them. We then introduce three taxonomies drawn from this analysis
as well as covering over one hundred interdisciplinary research papers about
moderation strategies. We identified the differences between the content
moderation employed in mainstream social media platforms compared to fringe
platforms. We also highlight the implications of Section 230, the need for
transparency and opacity in content moderation, why platforms should shift from
a one-size-fits-all model to a more inclusive model, and lastly, we highlight
why there is a need for a collaborative human-AI system
Politische Maschinen: Maschinelles Lernen für das Verständnis von sozialen Maschinen
This thesis investigates human-algorithm interactions in sociotechnological ecosystems. Specifically, it applies machine learning and statistical methods to uncover political dimensions of algorithmic influence in social media platforms and automated decision making systems. Based on the results, the study discusses the legal, political and ethical consequences of algorithmic implementations.Diese Arbeit untersucht Mensch-Algorithmen-Interaktionen in sozio-technologischen Ă–kosystemen. Sie wendet maschinelles Lernen und statistische Methoden an, um politische Dimensionen des algorithmischen Einflusses auf Socialen Medien und automatisierten Entscheidungssystemen aufzudecken. Aufgrund der Ergebnisse diskutiert die Studie die rechtlichen, politischen und ethischen Konsequenzen von algorithmischen Anwendungen
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