Assessing Symptoms of Excessive SNS Usage Based on User Behavior and Emotion

Intapong, Ploypailin; Charoenpit, Saromporn; Achalakul, Tiranee; Ohkura, Michiko

doi:10.1007/978-3-319-58559-8_7

Cited by 4 publications

(1 citation statement)

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“…Social media platforms provide a medium for individuals or a group to connect with the world and share their opinion (Intapong et al. 2017), often with limited inhibitions. Taking the advantage of such liberation, social media users may use vulgar, pornographic, or hateful language.…”

Section: Introductionmentioning

confidence: 99%

Emojis as anchors to detect Arabic offensive language and hate speech

Mubarak,

Hassan,

Chowdhury

2023

Nat. Lang. Eng.

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We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to collect a large number of offensive tweets. We apply the proposed method on Arabic tweets and compare it with English tweets—analyzing key cultural differences. We observed a constant usage of these emojis to represent offensiveness throughout different timespans on Twitter. We manually annotate and publicly release the largest Arabic dataset for offensive, fine-grained hate speech, vulgar, and violence content. Furthermore, we benchmark the dataset for detecting offensiveness and hate speech using different transformer architectures and perform in-depth linguistic analysis. We evaluate our models on external datasets—a Twitter dataset collected using a completely different method, and a multi-platform dataset containing comments from Twitter, YouTube, and Facebook, for assessing generalization capability. Competitive results on these datasets suggest that the data collected using our method capture universal characteristics of offensive language. Our findings also highlight the common words used in offensive communications, common targets for hate speech, specific patterns in violence tweets, and pinpoint common classification errors that can be attributed to limitations of NLP models. We observe that even state-of-the-art transformer models may fail to take into account culture, background, and context or understand nuances present in real-world data such as sarcasm.

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Section: Introductionmentioning

confidence: 99%