Bert Model for Social Media Bot Detection
| dc.contributor.author | Heidari, Maryam | |
| dc.contributor.author | Jones, James H Jr. | |
| dc.date.accessioned | 2022-03-11T01:23:06Z | |
| dc.date.available | 2022-03-11T01:23:06Z | |
| dc.date.issued | 2022-03 | |
| dc.description.abstract | Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event, or product. However, this use raises an important question: what percentage of the information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a ``bot" instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. This paper introduces a new model that uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features for the social media bot detection model. Using a Natural Language Processing approach to derive topic-independent features for the new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data set Cresci \cite{cresci-etal-2017-paradigm}as generated by a bot or a human, where the most accurate prior work achieved an accuracy of 92\%. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12756 | |
| dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/3.0/us/ | |
| dc.subject | Bot detection | |
| dc.subject | Natural Language Processing | |
| dc.subject | Neural Network | |
| dc.subject | Social Media | |
| dc.title | Bert Model for Social Media Bot Detection | |
| dc.type | Working Paper |