Researchers from the Department of Communication at UCLA have developed a system for identifying and eliminating clickbait from social media.
Clickbait refers to content whose main purpose is to attract attention and encourage visitors to click on a link. It can come in a variety of ways such as provocative images or enticing titles. Clickbait usually leads to sites that try to sell you something or to possibly extort you. At the very least, clickbait annoys users with its non-relevant ads and can waste their time by having them click on the link to be disappointed by what is behind it. Clickbait has now become more rampant with the rise in popularity of social media, and presents a problem for social media feeds.
Researchers from the Department of Communication at UCLA have developed a system for identifying and eliminating clickbait from social media. Their invention extracts features of social media posts, such as the image, text, and other meta features, and then determines whether or not it is clickbait using a classifier. This approach proves robust due to the fact that it collects a holistic view of the post rather than just looking at the image or text alone. If the post falls under clickbait, then the system associates the data extracted from the clickbait to the user who posted it. This system can then provide its user an in-depth analysis of the clickbait it has encountered. Furthermore this system can clean its user’s social media feed to eliminate posts made by users who post clickbait.