Abstract Number:
3740
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Jake Koerner (1), Ana-Maria Staicu (2), William Rand (2), Zakaria Babutsidze (3)
Institutions:
(1) N/A, N/A, (2) North Carolina State University, N/A, (3) SKEMA Business School, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
This work studies a year of posting behavior of social media users interacting with bot accounts and how their behavior differs from that of matched users that do not interact with bots. The posting behavior is described by a combination of the user's weekly number of posts, words, ats, tags, and links. We propose a flexible model for the posting behavior of users and introduce a hypothesis testing framework to formally assess if there is evidence that a new user, whose posting behavior has been observed repeatedly, is susceptible to bot interaction.
Our methodology relies on two latent processes, one indicating whether an account is active during a week, and another quantifying the posting behavior during active weeks. Using a powerful testing procedure, we perform testing separately for the activity behavior and for the posting behavior, using a multiple testing correction to ensure the overall type I error rate. The method allows insights into how posting behavior of a new user flagged as susceptible differs from that of a non-susceptible one. The proposed methodology is investigated in finite samples through simulations, including scenarios that mimic the data application.
Keywords:
Functional Data Analysis|Social Media|Testing|Social Bot Interaction|Posting Behavior |
Sponsors:
Section on Nonparametric Statistics
Tracks:
Statistical Methods for Functional Data
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