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Found 5 papers, 1 papers with code
Lumen: A Machine Learning Framework to Expose Influence Cues in Text
Phishing and disinformation are popular social engineering attacks with attackers invariably applying influence cues in texts to make them more appealing to users.
Predicting Different Types of Subtle Toxicity in Unhealthy Online Conversations
This paper investigates the use of machine learning models for the classification of unhealthy online conversations containing one or more forms of subtler abuse, such as hostility, sarcasm, and generalization.
Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles
This paper investigates whether computer usage profiles comprised of process-, network-, mouse-, and keystroke-related events are unique and consistent over time in a naturalistic setting, discussing challenges and opportunities of using such profiles in applications of continuous authentication.
Facebook Ad Engagement in the Russian Active Measures Campaign of 2016
This paper examines 3, 517 Facebook ads created by Russia's Internet Research Agency (IRA) between June 2015 and August 2017 in its active measures disinformation campaign targeting the 2016 U. S. general election.
People Still Care About Facts: Twitter Users Engage More with Factual Discourse than Misinformation--A Comparison Between COVID and General Narratives on Twitter
We found that (i) factual tweets, regardless of whether COVID-related, were more engaging than misinformation tweets; and (ii) features that most heavily correlated with engagement varied depending on the veracity and content of the tweet.
