12,217 research outputs found
Closing the loop: assisting archival appraisal and information retrieval in one sweep
In this article, we examine the similarities between the concept of appraisal, a process that takes place within the archives, and the concept of relevance judgement, a process fundamental to the evaluation of information retrieval systems. More specifically, we revisit selection criteria proposed as result of archival research, and work within the digital curation communities, and, compare them to relevance criteria as discussed within information retrieval's literature based discovery. We illustrate how closely these criteria relate to each other and discuss how understanding the relationships between the these disciplines could form a basis for proposing automated selection for archival processes and initiating multi-objective learning with respect to information retrieval
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Online knowledge repositories typically rely on their users or dedicated
editors to evaluate the reliability of their content. These evaluations can be
viewed as noisy measurements of both information reliability and information
source trustworthiness. Can we leverage these noisy evaluations, often biased,
to distill a robust, unbiased and interpretable measure of both notions?
In this paper, we argue that the temporal traces left by these noisy
evaluations give cues on the reliability of the information and the
trustworthiness of the sources. Then, we propose a temporal point process
modeling framework that links these temporal traces to robust, unbiased and
interpretable notions of information reliability and source trustworthiness.
Furthermore, we develop an efficient convex optimization procedure to learn the
parameters of the model from historical traces. Experiments on real-world data
gathered from Wikipedia and Stack Overflow show that our modeling framework
accurately predicts evaluation events, provides an interpretable measure of
information reliability and source trustworthiness, and yields interesting
insights about real-world events.Comment: Accepted at 26th World Wide Web conference (WWW-17
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
Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes
This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception
Evaluating trust in electronic commerce : a study based on the information provided on merchants' websites
Lack of trust has been identified as a major problem hampering the growth of Electronic Commerce (EC). It is reported by many studies that a large number of online shoppers abandon their transactions because they do not trust the website when they are asked to provide personal information. To support trust, we developed an information framework model based on research on EC trust. The model is based on the information a consumer expects to find on an EC website and that is shown from the literature to increase his/her trust towards online merchants. An information extraction system is then developed to help the user find this information. In this paper, we present the development of the information extraction system and its evaluation. This is then followed by a study looking at the use of the identified variables on a sample of EC websites
Is Social Media a Threat or Can It Be a Trusted Agent?
There is a prevailing belief within the United States Department of Defense (DOD) that social media is a threat to national security, leading to restrictions in workplace use of social-media applications. However, instead of dismissing social media as a threat, leaders should be asking whether or not the information received via social media can be trusted, thus leveraging the information-sharing capabilities of social media. This article presents a theoretical case for quantifying social media trustworthiness by exploring the factors that influence trust in social media and proposing a trust framework to be used to quantify trustworthiness
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