Search Results for author:
Found 16 papers, 0 papers with code
Evaluating Trustworthiness of Online News Publishers via Article Classification
The proliferation of low-quality online information in today's era has underscored the need for robust and automatic mechanisms to evaluate the trustworthiness of online news publishers.
From Online Behaviours to Images: A Novel Approach to Social Bot Detection
Online Social Networks have revolutionized how we consume and share information, but they have also led to a proliferation of content not always reliable and accurate.
Demystifying Misconceptions in Social Bots Research
Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation.
A Structured Analysis of Journalistic Evaluations for News Source Reliability
In today's era of information disorder, many organizations are moving to verify the veracity of news published on the web and social media.
Adversarial machine learning for protecting against online manipulation
Adversarial examples are inputs to a machine learning system that result in an incorrect output from that system.
A Behavioural Analysis of Credulous Twitter Users
Thanks to platforms such as Twitter and Facebook, people can know facts and events that otherwise would have been silenced.
Social and Information Networks
Improving Opinion Spam Detection by Cumulative Relative Frequency Distribution
Over the last years, online reviews became very important since they can influence the purchase decision of consumers and the reputation of businesses, therefore, the practice of writing fake reviews can have severe consequences on customers and service providers.
Who framed Roger Reindeer? De-censorship of Facebook posts by snippet classification
However, being the collaborative web characterised by a redundancy of information, it is not unusual that the same fact is reported by multiple sources, which may not apply the same restriction policies in terms of censorship.
A study on text-score disagreement in online reviews
In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score.
Mining Worse and Better Opinions. Unsupervised and Agnostic Aggregation of Online Reviews
In this paper, we propose a novel approach for aggregating online reviews, according to the opinions they express.
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots.
Semi-supervised knowledge extraction for detection of drugs and their effects
Based on the very small set of initial seeds, the work highlights how a contrastive approach and context deduction are effective in detecting substances and effects from the corpora.
A matter of words: NLP for quality evaluation of Wikipedia medical articles
We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain.
DNA-inspired online behavioral modeling and its application to spambot detection
We propose a strikingly novel, simple, and effective approach to model online user behavior: we extract and analyze digital DNA sequences from user online actions and we use Twitter as a benchmark to test our proposal.
Fame for sale: efficient detection of fake Twitter followers
$\textit{Fake followers}$ are those Twitter accounts specifically created to inflate the number of followers of a target account.
