Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media

Conference ·
Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence; (ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure; and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1891407
Country of Publication:
United States
Language:
English

References (16)

Information and Disinformation: Social Media in the COVID‐19 Crisis journal June 2020
Identification of influential spreaders in complex networks journal August 2010
Information transfer in social media conference April 2012
Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media journal January 2021
Analysis of Online Social Network Connections for Identification of Influential Users journal January 2018
Measuring User Influence in Twitter: The Million Follower Fallacy journal May 2010
GeoCoV19 journal June 2020
A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy journal July 2020
Entropy-Based Social Influence Evaluation in Mobile Social Networks book January 2015
The “Pandemic” of Disinformation in COVID-19 journal August 2020
Inferring mechanisms of response prioritization on social media under information overload journal January 2021
Automatic detection of influential actors in disinformation networks journal January 2021
The effects of information overload on online conversation dynamics journal June 2020
Ranking spreaders by decomposing complex networks journal June 2013
Influence analysis in social networks: A survey journal March 2018
Snorkel: rapid training data creation with weak supervision journal July 2019

Similar Records

Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media
Conference · Fri Apr 27 00:00:00 EDT 2018 · OSTI ID:1435892

Machine Intelligence to Detect, Characterise, and Defend against Influence Operations in the Information Environment
Journal Article · Wed Apr 14 00:00:00 EDT 2021 · Journal of Information Warfare · OSTI ID:1777157

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study
Journal Article · Mon May 24 20:00:00 EDT 2021 · Journal of Medical Internet Research · OSTI ID:1827580

Related Subjects