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Predicting Clickbait Strength in Online Social Media ...
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Abstract:
Hoping for a large number of clicks and potentially high social shares, journalists of various news media outlets publish sensationalist headlines on social media. These headlines lure the readers to click on them and satisfy the curiosity gap in their mind. Low quality material pointed to by clickbaits leads to time wastage and annoyance for users. Even for enterprises publishing clickbaits, it hurts more than it helps as it erodes user trust, attracts wrong visitors, and produces negative signals for ranking algorithms. Hence, identifying and flagging clickbait titles is very essential. Previous work on clickbaits has majorly focused on binary classification of clickbait titles. However not all clickbaits are equally clickbaity. It is not only essential to identify a clickbait, but also to identify the intensity of the clickbait based on the strength of the clickbait. In this work, we model clickbait strength prediction as a regression problem. While previous methods have relied on traditional machine ...
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Keyword:
Computer and Information Science; Natural Language Processing; Neural Network
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URL: https://underline.io/lecture/6174-predicting-clickbait-strength-in-online-social-media https://dx.doi.org/10.48448/rqz9-ge18
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Multi-label Categorization of Accounts of Sexism using a Neural Framework ...
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