625 research outputs found
A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections
Recently, messaging applications, such as WhatsApp, have been reportedly
abused by misinformation campaigns, especially in Brazil and India. A notable
form of abuse in WhatsApp relies on several manipulated images and memes
containing all kinds of fake stories. In this work, we performed an extensive
data collection from a large set of WhatsApp publicly accessible groups and
fact-checking agency websites. This paper opens a novel dataset to the research
community containing fact-checked fake images shared through WhatsApp for two
distinct scenarios known for the spread of fake news on the platform: the 2018
Brazilian elections and the 2019 Indian elections.Comment: 7 pages. This is a preprint version of an accepted paper on ICWSM'20.
Please, consider to cite the conference version instead of this on
Helping Fact-Checkers Identify Fake News Stories Shared through Images on WhatsApp
WhatsApp has introduced a novel avenue for smartphone users to engage with
and disseminate news stories. The convenience of forming interest-based groups
and seamlessly sharing content has rendered WhatsApp susceptible to the
exploitation of misinformation campaigns. While the process of fact-checking
remains a potent tool in identifying fabricated news, its efficacy falters in
the face of the unprecedented deluge of information generated on the Internet
today. In this work, we explore automatic ranking-based strategies to propose a
"fakeness score" model as a means to help fact-checking agencies identify fake
news stories shared through images on WhatsApp. Based on the results, we design
a tool and integrate it into a real system that has been used extensively for
monitoring content during the 2018 Brazilian general election. Our experimental
evaluation shows that this tool can reduce by up to 40% the amount of effort
required to identify 80% of the fake news in the data when compared to current
mechanisms practiced by the fact-checking agencies for the selection of news
stories to be checked.Comment: This is a preprint version of an accepted manuscript on the Brazilian
Symposium on Multimedia and the Web (WebMedia). Please, consider to cite it
instead of this on
Understanding mobility in networks
Motivated by the growing number of mobile devices capable of connecting and exchanging messages, we propose a methodology aiming to model and analyze node mobility in networks. We note that many existing solutions in the literature rely on topological measurements calculated directly on the graph of node contacts, aiming to capture the notion of the node's importance in terms of connectivity and mobility patterns beneficial for prototyping, design, and deployment of mobile networks. However, each measure has its specificity and fails to generalize the node importance notions that ultimately change over time. Unlike previous approaches, our methodology is based on a node embedding method that models and unveils the nodes' importance in mobility and connectivity patterns while preserving their spatial and temporal characteristics. We focus on a case study based on a trace of group meetings. The results show that our methodology provides a rich representation for extracting different mobility and connectivity patterns, which can be helpful for various applications and services in mobile networks
On network backbone extraction for modeling online collective behavior
Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation
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