The phrase embeddings are then used to iteratively develop the seed set in line with the cosine similarity based mostly criteria outlined in Vijayaraghavan et al. 2016). Refer to Dash et al. We further manually annotate the subsample to establish harmful tweets. 2021) for the specific key phrases used to classify the tweets into the different occasions. As a consequence of the large quantity of tweets for each of the occasions, we use a lexicon primarily based method to sample a subset of the event-specific tweets which might be likely to be utilized in an inflammatory context. One of the distinguishing points of dangerous speech is context, particularly “social or historical context that has lowered the boundaries to violence or made it extra acceptable…
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Two annotators annotate every tweet, based on the dangerous speech framework in Mission (2021) in addition to their contextual data of the occasions and the Indian political panorama. We report scores of 0.92, 0.73 and 0.88 for CAA/NRC, COVID-19 and Farmers’ Protests respectively. As a result of extremely subjective nature of dangerous speech, we additional label a tweet as dangerous only if both annotators agree on the label for the tweet. We measure inter-annotator reliability utilizing the Cohen’s Kappa coefficient. Note that we outline a dangerous person as any person that has tweeted no less than one tweet which has been categorized as harmful.
Certainly, in their work on social media strategies adopted by Indian political events, Mahapatra and Plagemann (2019) warn that political leadersâ rising dissemination of polarising content has immense ramifications for democracy. We describe the information assortment methodology, quantification of harmful speech and characterisation of harmful speech users here. The complete pipeline could be present in Figure 3. The various attributes of the harmful speech framework are captured in several levels of the pipeline.
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Dangerous speech on social media platforms could be framed as blatantly inflammatory, or be couched in innuendo. Additionally it is centrally tied to who engages it – it can be driven by openly sectarian social media accounts, or via delicate nudges by influential accounts, allowing for advanced means of reinforcing vilification of marginalized groups, an increasingly vital downside within the media environment in the worldwide South. We identify dangerous speech by influential accounts on Twitter in India round three key events, examining both the language and networks of messaging that condones or actively promotes violence in opposition to weak groups.
Moreover, we talk about the position of influential users in propagating dangerous speech and contextualize our research inside Indiaâs political landscape. Scholarly attention given to hate speech within online social networks. The usage of computational approaches in the direction of the research of this phenomena can be an efficient means to know the character of socialised ire in numerous societies and mirror on pathways to cut back hurt. 3.1. A rise in public consciousness over the previous decade has paralleled the media.