[LOGOS] Wednesday Meetup, 2 Talks!

[Emiliano De Cristofaro]

Hi Everyone,

We will have two talks at our next meetup on Wednesday at 2 p.m. in Bourns
A171 (or on Zoom
<https://ucr.zoom.us/j/94346366729?pwd=dWkwSVdwcTFoVGlPdnhTdHlpRnV1Zz09>).

Arman and Hammas will present their upcoming ICWSM'24
<http://icwsm.org/2024/index.html/> papers -- please find details below.

We hope to see as many of you as possible in person!

Cheers,
Emiliano

*Talk 1: ArguSense: Argument-Centric Analysis of Online Discourse, Arman
Irani*

How can we model arguments and their dynamics in online forum discussions?
The meteoric rise of online forums presents researchers across different
disciplines with an unprecedented opportunity: we have access to texts
containing discourse between groups of users generated in a voluntary and
organic fashion. Most prior work so far has focused on classifying
individual monological comments as either argumentative or not
argumentative. However, few efforts quantify and describe the dialogical
processes between users found in online forum discourse: the structure and
content of interpersonal argumentation. Modeling dialogical discourse
requires the ability to identify the presence of arguments, group them into
clusters, and summarize the content and nature of clusters of arguments
within a discussion thread in the forum. In this work, we develop
ArguSense, a comprehensive and systematic framework for understanding
arguments and debate in online forums. Our framework consists of methods
for, among other things: (a) detecting argument topics in an unsupervised
manner; (b) describing the structure of arguments within threads with
powerful visualizations; and (c) quantifying the content and diversity of
threads using argument similarity and clustering algorithms. We showcase
our approach by analyzing the discussions of four communities on the Reddit
platform over a span of 21 months. Specifically, we analyze the structure
and content of threads related to GMOs in forums related to agriculture or
farming to demonstrate the value of our framework.

*Talk 2: TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos
to their Source Communities, Mohammad Hammas Saeed*

Alas, coordinated hate attacks, or raids, are becoming increasingly common
online. In a nutshell, these are perpetrated by a group of aggressors who
organize and coordinate operations on a platform (e.g., 4chan) to target
victims on another community (e.g., YouTube). In this paper, we focus on
attributing raids to their source community, paving the way for moderation
approaches that take the context (and potentially the motivation) of an
attack into consideration. We present TUBERAIDER, an attribution system
achieving over 75% accuracy in detecting and attributing coordinated hate
attacks on YouTube videos. We instantiate it using links to YouTube videos
shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related
subreddits. We use a peak detector to identify a rise in the comment
activity of a YouTube video, which signals that an attack may be occurring.
We then train a machine learning classifier based on the community language
(i.e., TF-IDF scores of relevant keywords) to perform the attribution. We
test TUBERAIDER in the wild and present a few case studies of actual
aggression attacks identified by it to showcase its effectiveness.

*Bios*

Arman Irani is a third-year Ph.D. candidate specializing in Natural
Language Processing within the Computer Science Department at the
University of California, Riverside. His research focus revolves around
developing sophisticated methods tailored for the in-depth analysis of
argumentation, deliberation patterns, and the intricate representation of
belief narratives embedded in online textual sources, particularly within
online forums and digital discourse platforms.

Hammas is a 5th year PhD Student at Boston University. He is also a core
member of the Security Lab (SeclaBU) and collaborates with the iDrama Lab.
His research lies on the cusp of cybersafety, applied machine learning and
social computing, where he aims at understanding, detecting, and combating
online harms using a data-driven approach. His research process involves
identifying pertinent issues within cyberspace and developing tools to
mitigate the malicious behavior with the goal of offering policymakers
guidelines to ensure user safety on social media platforms. His work has
been published in top-tier venues in security and web measurement,
including IEEE S&P, ICWSM, WebSci and has been reported by Wired.
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