5 research outputs found
Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach
The rise of social media platforms has facilitated the formation of echo
chambers, which are online spaces where users predominantly encounter
viewpoints that reinforce their existing beliefs while excluding dissenting
perspectives. This phenomenon significantly hinders information dissemination
across communities and fuels societal polarization. Therefore, it is crucial to
develop methods for quantifying echo chambers. In this paper, we present the
Echo Chamber Score (ECS), a novel metric that assesses the cohesion and
separation of user communities by measuring distances between users in the
embedding space. In contrast to existing approaches, ECS is able to function
without labels for user ideologies and makes no assumptions about the structure
of the interaction graph. To facilitate measuring distances between users, we
propose EchoGAE, a self-supervised graph autoencoder-based user embedding model
that leverages users' posts and the interaction graph to embed them in a manner
that reflects their ideological similarity. To assess the effectiveness of ECS,
we use a Twitter dataset consisting of four topics - two polarizing and two
non-polarizing. Our results showcase ECS's effectiveness as a tool for
quantifying echo chambers and shedding light on the dynamics of online
discourse.Comment: 9 Pages, 3 Figure
PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework
Hate speech detection refers to the task of detecting hateful content that
aims at denigrating an individual or a group based on their religion, gender,
sexual orientation, or other characteristics. Due to the different policies of
the platforms, different groups of people express hate in different ways.
Furthermore, due to the lack of labeled data in some platforms it becomes
challenging to build hate speech detection models. To this end, we revisit if
we can learn a generalizable hate speech detection model for the cross platform
setting, where we train the model on the data from one (source) platform and
generalize the model across multiple (target) platforms. Existing
generalization models rely on linguistic cues or auxiliary information, making
them biased towards certain tags or certain kinds of words (e.g., abusive
words) on the source platform and thus not applicable to the target platforms.
Inspired by social and psychological theories, we endeavor to explore if there
exist inherent causal cues that can be leveraged to learn generalizable
representations for detecting hate speech across these distribution shifts. To
this end, we propose a causality-guided framework, PEACE, that identifies and
leverages two intrinsic causal cues omnipresent in hateful content: the overall
sentiment and the aggression in the text. We conduct extensive experiments
across multiple platforms (representing the distribution shift) showing if
causal cues can help cross-platform generalization.Comment: ECML PKDD 202
Domain Generalization -- A Causal Perspective
Machine learning models rely on various assumptions to attain high accuracy.
One of the preliminary assumptions of these models is the independent and
identical distribution, which suggests that the train and test data are sampled
from the same distribution. However, this assumption seldom holds in the real
world due to distribution shifts. As a result models that rely on this
assumption exhibit poor generalization capabilities. Over the recent years,
dedicated efforts have been made to improve the generalization capabilities of
these models collectively known as -- \textit{domain generalization methods}.
The primary idea behind these methods is to identify stable features or
mechanisms that remain invariant across the different distributions. Many
generalization approaches employ causal theories to describe invariance since
causality and invariance are inextricably intertwined. However, current surveys
deal with the causality-aware domain generalization methods on a very
high-level. Furthermore, we argue that it is possible to categorize the methods
based on how causality is leveraged in that method and in which part of the
model pipeline is it used. To this end, we categorize the causal domain
generalization methods into three categories, namely, (i) Invariance via Causal
Data Augmentation methods which are applied during the data pre-processing
stage, (ii) Invariance via Causal representation learning methods that are
utilized during the representation learning stage, and (iii) Invariance via
Transferring Causal mechanisms methods that are applied during the
classification stage of the pipeline. Furthermore, this survey includes
in-depth insights into benchmark datasets and code repositories for domain
generalization methods. We conclude the survey with insights and discussions on
future directions
Causal Learning for Socially Responsible AI
There have been increasing concerns about Artificial Intelligence (AI) due to
its unfathomable potential power. To make AI address ethical challenges and
shun undesirable outcomes, researchers proposed to develop socially responsible
AI (SRAI). One of these approaches is causal learning (CL). We survey
state-of-the-art methods of CL for SRAI. We begin by examining the seven CL
tools to enhance the social responsibility of AI, then review how existing
works have succeeded using these tools to tackle issues in developing SRAI such
as fairness. The goal of this survey is to bring forefront the potentials and
promises of CL for SRAI.Comment: 8 pages, 3 figures, accepted at IJCAI21 survey trac
Exploring Platform Migration Patterns between Twitter and Mastodon: A User Behavior Study
A recent surge of users migrating from Twitter to alternative platforms, such
as Mastodon, raised questions regarding what migration patterns are, how
different platforms impact user behaviors, and how migrated users settle in the
migration process. In this study, we elaborate how we investigate these
questions by collecting data over 10,000 users who migrated from Twitter to
Mastodon within the first ten weeks following Elon Musk's acquisition of
Twitter. Our research is structured in three primary steps. First, we develop
algorithms to extract and analyze migration patters. Second, by leveraging
behavioral analysis, we examine the distinct architectures of Twitter and
Mastodon to learn how different platforms shape user behaviors on each
platform. Last, we determine how particular behavioral factors influence users
to stay on Mastodon. We share our findings of user migration, insights, and
lessons learned from the user behavior study