1,362 research outputs found
Multilingual Cross-domain Perspectives on Online Hate Speech
In this report, we present a study of eight corpora of online hate speech, by
demonstrating the NLP techniques that we used to collect and analyze the
jihadist, extremist, racist, and sexist content. Analysis of the multilingual
corpora shows that the different contexts share certain characteristics in
their hateful rhetoric. To expose the main features, we have focused on text
classification, text profiling, keyword and collocation extraction, along with
manual annotation and qualitative study.Comment: 24 page
Deep Learning for User Comment Moderation
Experimenting with a new dataset of 1.6M user comments from a Greek news
portal and existing datasets of English Wikipedia comments, we show that an RNN
outperforms the previous state of the art in moderation. A deep,
classification-specific attention mechanism improves further the overall
performance of the RNN. We also compare against a CNN and a word-list baseline,
considering both fully automatic and semi-automatic moderation
Detecting Online Hate Speech Using Both Supervised and Weakly-Supervised Approaches
In the wake of a polarizing election, social media is laden with hateful content. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. We provide an annotated corpus of hate speech with context information well kept. Then we propose two types of supervised hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Further, to address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for online hate speech detection by leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language
Assessing the impact of contextual information in hate speech detection
In recent years, hate speech has gained great relevance in social networks
and other virtual media because of its intensity and its relationship with
violent acts against members of protected groups. Due to the great amount of
content generated by users, great effort has been made in the research and
development of automatic tools to aid the analysis and moderation of this
speech, at least in its most threatening forms. One of the limitations of
current approaches to automatic hate speech detection is the lack of context.
Most studies and resources are performed on data without context; that is,
isolated messages without any type of conversational context or the topic being
discussed. This restricts the available information to define if a post on a
social network is hateful or not. In this work, we provide a novel corpus for
contextualized hate speech detection based on user responses to news posts from
media outlets on Twitter. This corpus was collected in the Rioplatense
dialectal variety of Spanish and focuses on hate speech associated with the
COVID-19 pandemic. Classification experiments using state-of-the-art techniques
show evidence that adding contextual information improves hate speech detection
performance for two proposed tasks (binary and multi-label prediction). We make
our code, models, and corpus available for further research
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