35,574 research outputs found
Fake News Detection in Social Networks via Crowd Signals
Our work considers leveraging crowd signals for detecting fake news and is
motivated by tools recently introduced by Facebook that enable users to flag
fake news. By aggregating users' flags, our goal is to select a small subset of
news every day, send them to an expert (e.g., via a third-party fact-checking
organization), and stop the spread of news identified as fake by an expert. The
main objective of our work is to minimize the spread of misinformation by
stopping the propagation of fake news in the network. It is especially
challenging to achieve this objective as it requires detecting fake news with
high-confidence as quickly as possible. We show that in order to leverage
users' flags efficiently, it is crucial to learn about users' flagging
accuracy. We develop a novel algorithm, DETECTIVE, that performs Bayesian
inference for detecting fake news and jointly learns about users' flagging
accuracy over time. Our algorithm employs posterior sampling to actively trade
off exploitation (selecting news that maximize the objective value at a given
epoch) and exploration (selecting news that maximize the value of information
towards learning about users' flagging accuracy). We demonstrate the
effectiveness of our approach via extensive experiments and show the power of
leveraging community signals for fake news detection
Mapping the Money in Public Media
Provides an overview of emerging "user-centric" business models for public media that utilize the interactivity of digital technologies as a way to integrate content, communication, commerce, and community through participatory media creation
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
SUPER: Towards the Use of Social Sensors for Security Assessments and Proactive Management of Emergencies
Social media statistics during recent disasters (e.g. the 20 million tweets relating to 'Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap
Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision
Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark
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