3 research outputs found
Multitask Learning for Blackmarket Tweet Detection
Online social media platforms have made the world more connected than ever
before, thereby making it easier for everyone to spread their content across a
wide variety of audiences. Twitter is one such popular platform where people
publish tweets to spread their messages to everyone. Twitter allows users to
Retweet other users' tweets in order to broadcast it to their network. The more
retweets a particular tweet gets, the faster it spreads. This creates
incentives for people to obtain artificial growth in the reach of their tweets
by using certain blackmarket services to gain inorganic appraisals for their
content.
In this paper, we attempt to detect such tweets that have been posted on
these blackmarket services in order to gain artificially boosted retweets. We
use a multitask learning framework to leverage soft parameter sharing between a
classification and a regression based task on separate inputs. This allows us
to effectively detect tweets that have been posted to these blackmarket
services, achieving an F1-score of 0.89 when classifying tweets as blackmarket
or genuine.Comment: 4 pages, IEEE/ACM International Conference on Social Networks
Analysis and Mining (ASONAM) 201