Source-linked AI summary
Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter
Stefanie Haustein, Timothy D. Bowman, Kim Holmberg, Andrew Tsou, Cassidy R. Sugimoto, Vincent Larivière
TL;DR
This paper examines the largely unstudied presence of automated accounts distributing arXiv papers and its implications for Twitter-based research-impact measures. Using a manual classification of 51 accounts and analyses of their activity, it finds that these accounts produce substantial tweets, differ from common social bots, and complicate interpretation of raw tweet counts.
Problem
Automated Twitter accounts in scholarly communication had received little empirical investigation, despite concerns that robot tweeting and easily gamed social media metrics could undermine altmetrics.
Method
The authors manually classified 90 accounts found through Twitter searches for arXiv-related terms, retaining 51 accounts that distributed arXiv paper links and analyzing their activity and account types.
Results
Automated platform and topic feeds produced 97,429 tweets, while Bot or Not? scores were mostly below 50% and only three accounts were more likely bots than humans.
Takeaways & Limitations
Raw tweet counts should distinguish automated dissemination from human activity and account types or engagement levels when used as indicators of scholarly impact.
Takeaways & Limitations
The study focuses exclusively on Twitter and the arXiv preprint repository, so further research is needed on other platforms and the mechanisms underlying social-media impact counts.
Abstract
from arXiv · showhide
This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific papers deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. We present preliminary findings that automated Twitter accounts create a considerable amount of tweets to scientific papers and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose differentiating between different levels of engagement from tweeting only bibliographic information to discussing or commenting on the content of a paper.