Search Results for author:
Found 27 papers, 12 papers with code
Misinformation Resilient Search Rankings with Webgraph-based Interventions
The proliferation of unreliable news domains on the internet has had wide-reaching negative impacts on society.
Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?
Traffic simulators are used to generate data for learning in intelligent transportation systems (ITSs).
A Weakly Supervised Classifier and Dataset of White Supremacist Language
We present a dataset and classifier for detecting the language of white supremacist extremism, a growing issue in online hate speech.
Identity Construction in a Misogynist Incels Forum
Online communities of involuntary celibates (incels) are a prominent source of misogynist hate speech.
How Hate Speech Varies by Target Identity: A Computational Analysis
We also find that words associated with hate speech targeting specific identities often relate to stereotypes, histories of oppression, current social movements, and other social contexts specific to identities.
Multi-modal Networks Reveal Patterns of Operational Similarity of Terrorist Organizations
Capturing dynamics of operational similarity among terrorist groups is critical to provide actionable insights for counter-terrorism and intelligence monitoring.
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation
Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection.
Coordinating Narratives and the Capitol Riots on Parler
Coordinated disinformation campaigns are used to influence social media users, potentially leading to offline violence.
Learning future terrorist targets through temporal meta-graphs
Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets.
The Coronavirus is a Bioweapon: Analysing Coronavirus Fact-Checked Stories
The 2020 coronavirus pandemic has heightened the need to flag coronavirus-related misinformation, and fact-checking groups have taken to verifying misinformation on the Internet.
A Weakly Supervised Approach for Classifying Stance in Twitter Replies
Compared to supervised learning, our method improves the mean F1-macro by 8\% on the hand-labeled dataset without using any hand-labeled examples in the training set.
Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset
Finally, our sociolinguistic analyses suggest that COVID-19 informed users tend to use more narratives than misinformed users.
Bot-Match: Social Bot Detection with Recursive Nearest Neighbors Search
This social threat has spawned a bot detection algorithms race in which detection algorithms evolve in an attempt to keep up with increasingly sophisticated bot accounts.
Characterizing Sociolinguistic Variation in the Competing Vaccination Communities
For an effective health communication, it is imperative to focus on "preference-based framing" where the preferences of the target sub-community are taken into consideration.
Disinformation and Misinformation on Twitter during the Novel Coronavirus Outbreak
Tweets mentioning "fake news" URLs and disinformation story-lines are also more likely to be spread by regular users.
Social and Information Networks
Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations
Moreover, the existing datasets do not distinguish between the different types of conversations on social media (e. g., replying vs. quoting on Twitter).
A Computational Analysis of Polarization on Indian and Pakistani Social Media
Between February 14, 2019 and March 4, 2019, a terrorist attack in Pulwama, Kashmir followed by retaliatory airstrikes led to rising tensions between India and Pakistan, two nuclear-armed countries.
Discover Your Social Identity from What You Tweet: a Content Based Approach
We further propose a transfer learning scheme that improves our model's performance by a large margin.
A Hierarchical Location Prediction Neural Network for Twitter User Geolocation
In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation.
Graph-Hist: Graph Classification from Latent Feature Histograms With Application to Bot Detection
Social media is a critical application area in this space, however the characteristics of social media graphs differ from those seen in most popular benchmark datasets.
Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification
Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN).
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence.
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning
In this paper, we study the problem of node representation learning with graph neural networks.
On the Evolution of Spreading Processes in Complex Networks
In this paper, we investigate the evolution of spreading processes on complex networks with the aim of i) revealing the role of evolution on the threshold, probability, and final size of epidemics; and ii) exploring the interplay between the structural properties of the network and the dynamics of evolution.
Physics and Society Social and Information Networks
Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks
Aspect Based Sentiment Analysis, PyTorch Implementations.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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A Probabilistic Framework for Location Inference from Social Media
In this paper, we formalize the problem of inferring location from social media into a semi-supervised factor graph model (SSFGM).
