6 research outputs found
From the User to the Medium: Neural Profiling Across Web Communities
Online communities provide a unique way for individuals to access information
from those in similar circumstances, which can be critical for health
conditions that require daily and personalized management. As these groups and
topics often arise organically, identifying the types of topics discussed is
necessary to understand their needs. As well, these communities and people in
them can be quite diverse, and existing community detection methods have not
been extended towards evaluating these heterogeneities. This has been limited
as community detection methodologies have not focused on community detection
based on semantic relations between textual features of the user-generated
content. Thus here we develop an approach, NeuroCom, that optimally finds dense
groups of users as communities in a latent space inferred by neural
representation of published contents of users. By embedding of words and
messages, we show that NeuroCom demonstrates improved clustering and identifies
more nuanced discussion topics in contrast to other common unsupervised
learning approaches
A Topic-Agnostic Approach for Identifying Fake News Pages
Fake news and misinformation have been increasingly used to manipulate
popular opinion and influence political processes. To better understand fake
news, how they are propagated, and how to counter their effect, it is necessary
to first identify them. Recently, approaches have been proposed to
automatically classify articles as fake based on their content. An important
challenge for these approaches comes from the dynamic nature of news: as new
political events are covered, topics and discourse constantly change and thus,
a classifier trained using content from articles published at a given time is
likely to become ineffective in the future. To address this challenge, we
propose a topic-agnostic (TAG) classification strategy that uses linguistic and
web-markup features to identify fake news pages. We report experimental results
using multiple data sets which show that our approach attains high accuracy in
the identification of fake news, even as topics evolve over time.Comment: Accepted for publication in the Companion Proceedings of the 2019
World Wide Web Conference (WWW'19 Companion). Presented in the 2019
International Workshop on Misinformation, Computational Fact-Checking and
Credible Web (MisinfoWorkshop2019). 6 page