题名 |
Social Media Clickbait Detection based on Word Embedding Models |
DOI |
10.29428/9789860544169.201801.0016 |
作者 |
Vorakit Vorakitphan;Yao-Chung Fan |
关键词 |
Clickbait ; Word Embedding ; Classification ; Social Linguistic |
期刊名称 |
NCS 2017 全國計算機會議 |
卷期/出版年月 |
2017(2018 / 01 / 01) |
页次 |
78 - 82 |
内容语文 |
英文 |
中文摘要 |
In the past few years, social networking platform serves as a new media of news sharing and information diffusion. However, not all information provided in social platforms are beneficial for users. Some of the contents in social platforms are misleading the interested topics from users to poor web links with attractive headlines. Such behavior is referred to as Clickbait. The clickbait aims to bait users to visit poor content sites. Such poor contents from clickbait can invade user trusts from social networking platforms. In this work, we propose a novel approach based on word embedding model to detect clickbaits on social media platforms. Semantic relationships of the given words are preserved as a novel methodology by word embedding model which remarkably enhance clickbait detection on semantic-based. In addition, we conduct experiments with real data from twitters to validate the effectiveness of the proposed approach, which demonstrates that the employment of the word embedding model improve clickbait detection accuracy from 74% to 91% compared with the existing solutions. |
主题分类 |
基礎與應用科學 >
資訊科學 |