Real-time Detection of Content Polluters in Partially Observable Twitter Networks

TL;DR

This paper introduces a real-time methodology for detecting content polluters on Twitter during civil unrest events, relying solely on individual tweet data without requiring extensive network or historical information.

Contribution

The work presents a novel real-time detection approach for content polluters that does not depend on social network or historical account data, suitable for streaming social media analysis.

Findings

Identified peculiar characteristics of content polluters in the dataset

Proposed metrics for bot account detection

Evaluated Twitter's detection capabilities and existing methods

Abstract

Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of bot is particularly challenging, with state-of-the-art methods utilising large volumes of network data as features for machine learning models. Such datasets are generally not readily available in typical applications which stream social media data for real-time event prediction. In this work we develop a methodology to detect content polluters in social media datasets that are streamed in real-time. Applying our method to the problem of civil unrest event prediction in Australia, we identify content polluters from individual tweets, without collecting social network or historical data from individual accounts. We identify some peculiar characteristics of these bots in our dataset and propose metrics for identification of such accounts. We then pose some research questions around this type of bot detection, including: how good Twitter is at detecting content polluters and how well state-of-the-art methods perform in detecting bots in our dataset.