24 research outputs found
Analyzing Regrettable Communications on Twitter: Characterizing Deleted Tweets and Their Authors
Over 500 million tweets are posted in Twitter each day, out of which about
11% tweets are deleted by the users posting them. This phenomenon of widespread
deletion of tweets leads to a number of questions: what kind of content posted
by users makes them want to delete them later? %Are all users equally active in
deleting their tweets or Are users of certain predispositions more likely to
post regrettable tweets, deleting them later? In this paper we provide a
detailed characterization of tweets posted and then later deleted by their
authors. We collected tweets from over 200 thousand Twitter users during a
period of four weeks. Our characterization shows significant personality
differences between users who delete their tweets and those who do not. We find
that users who delete their tweets are more likely to be extroverted and
neurotic while being less conscientious. Also, we find that deleted tweets
while containing less information and being less conversational, contain
significant indications of regrettable content. Since users of online
communication do not have instant social cues (like listener's body language)
to gauge the impact of their words, they are often delayed in employing repair
strategies. Finally, we build a classifier which takes textual, contextual, as
well as user features to predict if a tweet will be deleted or not. The
classifier achieves a F1-score of 0.78 and the precision increases when we
consider response features of the tweets
Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models
Advances in deep neural networks (DNNs) have shown tremendous promise in the
medical domain. However, the deep learning tools that are helping the domain,
can also be used against it. Given the prevalence of fraud in the healthcare
domain, it is important to consider the adversarial use of DNNs in manipulating
sensitive data that is crucial to patient healthcare. In this work, we present
the design and implementation of a DNN-based image translation attack on
biomedical imagery. More specifically, we propose Jekyll, a neural style
transfer framework that takes as input a biomedical image of a patient and
translates it to a new image that indicates an attacker-chosen disease
condition. The potential for fraudulent claims based on such generated 'fake'
medical images is significant, and we demonstrate successful attacks on both
X-rays and retinal fundus image modalities. We show that these attacks manage
to mislead both medical professionals and algorithmic detection schemes.
Lastly, we also investigate defensive measures based on machine learning to
detect images generated by Jekyll.Comment: Published in proceedings of the 5th European Symposium on Security
and Privacy (EuroS&P '20
What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations
With over 500 million tweets posted per day, in Twitter, it is difficult for
Twitter users to discover interesting content from the deluge of uninteresting
posts. In this work, we present a novel, explainable, topical recommendation
system, that utilizes social annotations, to help Twitter users discover
tweets, on topics of their interest. A major challenge in using traditional
rating dependent recommendation systems, like collaborative filtering and
content based systems, in high volume social networks is that, due to attention
scarcity most items do not get any ratings. Additionally, the fact that most
Twitter users are passive consumers, with 44% users never tweeting, makes it
very difficult to use user ratings for generating recommendations. Further, a
key challenge in developing recommendation systems is that in many cases users
reject relevant recommendations if they are totally unfamiliar with the
recommended item. Providing a suitable explanation, for why the item is
recommended, significantly improves the acceptability of recommendation. By
virtue of being a topical recommendation system our method is able to present
simple topical explanations for the generated recommendations. Comparisons with
state-of-the-art matrix factorization based collaborative filtering, content
based and social recommendations demonstrate the efficacy of the proposed
approach
A Framework for Modeling Human Behavior in Large-scale Agent-based Epidemic Simulations
Acknowledgements We thank Cuebiq; mobility data is provided by Cuebiq, a location intelligence and measurement platform. Through its Data for Good program, Cuebiq provides access to aggregated mobility data for academic research and humanitarian initiatives. This first-party data is collected from anonymized users who have opted-in to provide access to their location data anonymously, through a GDPR and CCPA compliant framework. To further preserve privacy, portions of the data are aggregated to the census-block group level. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.Peer reviewedPublisher PD
Using Agent-Based Simulation to Investigate Behavioral Interventions in a Pandemic Simulating Behavioral Interventions in a Pandemic
Simulation is a useful tool for evaluating behavioral interventions when the adoption rate among a population is uncertain. Individual agent models are often prohibitively expensive, but, unlike stochastic models, allow studying compliance heterogeneity. In this paper we demonstrate the feasibility of evaluating behavioral intervention policies using large-scale data-driven agent-based simulations. We explain how the simulation is calibrated with respect to real-world data, and demonstrate the utility of our approach by studying the effectiveness of interventions used in Virginia in early 2020 through counterfactual simulations
Projected resurgence of COVID-19 in the United States in JulyâDecember 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination
In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for JulyâDecember 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in JulyâDecember 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during JulyâDecember, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model
Characterizing Deleted Tweets and Their Authors
This paper provides a detailed characterization of tweets posted and then later deleted by their authors, in the Twitter microblogging platform. Our characterization shows significant personality differences between users who delete their tweets and those who do not. We find that users who delete their tweets are more likely to be extroverted and neurotic while being less conscientious. Further, although deleted tweets contain more negative sentiment and swear words, they also show significant signs of being thoughtfully constructed
Learning a linear influence model between actors from transient opinion dynamics
Many social networks are characterized by actors (nodes) holding quantitative opinions about movies, songs, sports, people, colleges, politicians, and so on. These opinions are influenced by network neighbors. Many models have been proposed for such opinion dynamics, but they have some limitations. Most consider the strength of edge influence as fixed. Some model a discrete decision or action on part of each actor, and an edge as causing an ``infection'' (that is often permanent or self-resolving). Others model edge influence as a stochastic matrix to reuse the mathematics of eigensystems. Actors' opinions are usually observed globally and synchronously. Analysis usually skirts transient effects and focuses on steady-state behavior. There is very little direct experimental validation of estimated influence models. Here we initiate an investigation into new models that seek to remove these limitations. Our main goal is to estimate, not assume, edge influence strengths from an observed series of opinion values at nodes. We adopt a linear (but not stochastic) influence model. We make no assumptions about system stability or convergence. Further, actors' opinions may be observed in an asynchronous and incomplete fashion, after missing several time steps when an actor changed its opinion based on neighbors' influence. We present novel algorithms to estimate edge influence strengths while tackling these aggressively realistic assumptions. Experiments with Reddit, Twitter, and three social games we conducted on volunteers establish the promise of our algorithms. Our opinion estimation errors are dramatically smaller than strong baselines like the DeGroot, flocking, voter, and biased voter models. Our experiments also lend qualitative insights into asynchronous opinion updates and aggregation