Search Results for author:
Found 18 papers, 7 papers with code
Can Language Models Recognize Convincing Arguments?
The remarkable and ever-increasing capabilities of Large Language Models (LLMs) have raised concerns about their potential misuse for creating personalized, convincing misinformation and propaganda.
Prevalence and prevention of large language model use in crowd work
We show that the use of large language models (LLMs) is prevalent among crowd workers, and that targeted mitigation strategies can significantly reduce, but not eliminate, LLM use.
Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit
Overall, our findings suggest that curtailing fringe-interactions may reduce the growth of fringe communities on mainstream platforms.
ACTI at EVALITA 2023: Overview of the Conspiracy Theory Identification Task
Conspiracy Theory Identication task is a new shared task proposed for the first time at the Evalita 2023.
Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks
With the widespread adoption of LLMs, human gold--standard annotations are key to understanding the capabilities of LLMs and the validity of their results.
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science
Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks.
Understanding Online Migration Decisions Following the Banning of Radical Communities
Specifically, we quantify how these factors affect users' decisions to post on fringe platforms and, for those who do, whether they continue posting on the mainstream platform.
Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning
Using a difference-in-differences design, we contrast co-active users with matched counterparts to estimate the causal effect of fringe platform participation on users' antisocial behavior on Reddit.
Are Anti-Feminist Communities Gateways to the Far Right? Evidence from Reddit and YouTube
Our results suggest that there is a large overlap between the user bases of the Alt-right and of the Manosphere and that members of the Manosphere have a bigger chance to engage with far right content than carefully chosen counterparts.
Computers and Society
Volunteer contributions to Wikipedia increased during COVID-19 mobility restrictions
Wikipedia, the largest encyclopedia ever created, is a global initiative driven by volunteer contributions.
Computers and Society
YouNiverse: Large-Scale Channel and Video Metadata from English-Speaking YouTube
YouTube plays a key role in entertaining and informing people around the globe.
Time Series Analysis
Social and Information Networks
Computers and Society
Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis
Timely access to accurate information is crucial during the COVID-19 pandemic.
Social and Information Networks
The Evolution of the Manosphere Across the Web
In this paper, we present a large-scale characterization of the Manosphere, a conglomerate of Web-based misogynist movements roughly focused on "men's issues," which has seen significant growth over the past years.
Computers and Society
Auditing Radicalization Pathways on YouTube
Non-profits, as well as the media, have hypothesized the existence of a radicalization pipeline on YouTube, claiming that users systematically progress towards more extreme content on the platform.
Computers and Society Social and Information Networks
Automatic diagnosis of the 12-lead ECG using a deep neural network
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models.
Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network
We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency.
Characterizing and Detecting Hateful Users on Twitter
We observe that hateful users are densely connected, and thus formulate the hate speech detection problem as a task of semi-supervised learning over a graph, exploiting the network of connections on Twitter.
Computers and Society Social and Information Networks
Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition
We then use it to assign the latent values to the label values.
