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Found 71 papers, 27 papers with code
Using Word Embedding to Reveal Monetary Policy Explanation Changes
Documents have been an essential tool of communication for governments to announce their policy operations.
FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs
Our society is facing rampant misinformation harming public health and trust.
Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data
In response, we introduce a novel approach tailored for modeling social network data in user detection tasks.
Tracking the Newsworthiness of Public Documents
Journalists must find stories in huge amounts of textual data (e. g. leaks, bills, press releases) as part of their jobs: determining when and why text becomes news can help us understand coverage patterns and help us build assistive tools.
Leveraging Large Language Models to Detect Influence Campaigns in Social Media
Social media influence campaigns pose significant challenges to public discourse and democracy.
Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformation
We evaluated FACT-GPT on an extensive dataset of social media content related to public health.
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models
This article serves both as a synthesis of rigorous research presented on the risks of GenAI and misuse of LLMs and as a thought-provoking vision of the different types of harmful GenAI applications we might encounter in the near future, and some ways we can prepare for them.
Controlled Text Generation with Hidden Representation Transformations
We propose CHRT (Control Hidden Representation Transformation) - a controlled language generation framework that steers large language models to generate text pertaining to certain attributes (such as toxicity).
Identifying Informational Sources in News Articles
News articles are driven by the informational sources journalists use in reporting.
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models
As the capabilities of generative language models continue to advance, the implications of biases ingrained within these models have garnered increasing attention from researchers, practitioners, and the broader public.
Leveraging Social Interactions to Detect Misinformation on Social Media
Keeping track of the sequence of the interactions during the time, we improve over previous state-of-the-art models.
From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection
The COVID-19 pandemic has gained worldwide attention and allowed fake news, such as ``COVID-19 is the flu,'' to spread quickly and widely on social media.
Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls
The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm.
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment
In this work, we guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process.
GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports
In this paper we propose GCN-WP, a semi-supervised win prediction model for esports based on graph convolutional networks.
Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks
We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users.
Word Embedding for Social Sciences: An Interdisciplinary Survey
To extract essential information from complex data, computer scientists have been developing machine learning models that learn low-dimensional representation mode.
Extracting Fast and Slow: User-Action Embedding with Inter-temporal Information
We simultaneously embed the user's action sequence and its time intervals to obtain a low-dimensional representation of the action along with intertemporal information.
Zero-shot meta-learning for small-scale data from human subjects
While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled.
Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement
Malicious accounts spreading misinformation has led to widespread false and misleading narratives in recent times, especially during the COVID-19 pandemic, and social media platforms struggle to eliminate these contents rapidly.
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game
The popularity of online gaming has grown dramatically, driven in part by streaming and the billion-dollar e-sports industry.
FairFed: Enabling Group Fairness in Federated Learning
Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment.
Characterizing Online Engagement with Disinformation and Conspiracies in the 2020 U.S. Presidential Election
Identifying and characterizing disinformation in political discourse on social media is critical to ensure the integrity of elections and democratic processes around the world.
"Don't quote me on that": Finding Mixtures of Sources in News Articles
Journalists publish statements provided by people, or \textit{sources} to contextualize current events, help voters make informed decisions, and hold powerful individuals accountable.
Modeling "Newsworthiness" for Lead-Generation Across Corpora
Journalists obtain "leads", or story ideas, by reading large corpora of government records: court cases, proposed bills, etc.
Individualized Context-Aware Tensor Factorization for Online Games Predictions
Individual behavior and decisions are substantially influenced by their contexts, such as location, environment, and time.
Tracking e-cigarette warning label compliance on Instagram with deep learning
We conclude that deep learning models can effectively identify vaping posts on Instagram and track compliance with FDA warning label requirements.
Graph Signal Recovery Using Restricted Boltzmann Machines
We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine and the representational ability of a neural network.
Detecting Social Media Manipulation in Low-Resource Languages
Social media have been deliberately used for malicious purposes, including political manipulation and disinformation.
#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election
With social media often dictating the tones and trends of politics-related discussion, it is of paramount important to be able to study online chatter, especially in the run up to important voting events, like in the case of the upcoming November 3, 2020 U. S. Presidential Election.
Social and Information Networks
Leveraging Clickstream Trajectories to Reveal Low-Quality Workers in Crowdsourced Forecasting Platforms
In this study, we propose the use of a computational framework to identify clusters of underperforming workers using clickstream trajectories.
Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters
In this work, we propose a Sequence Reasoner with Round Attribute Encoder and Multi-Task Decoder to interpret the strategies behind the round-based purchasing decisions.
ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies.
Detecting multi-timescale consumption patterns from receipt data: A non-negative tensor factorization approach
Understanding consumer behavior is an important task, not only for developing marketing strategies but also for the management of economic policies.
TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers
We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings.
COVID-19: The First Public Coronavirus Twitter Dataset
At the time of this writing, the novel coronavirus (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources and economies around the world.
Social and Information Networks Populations and Evolution
Detecting Troll Behavior via Inverse Reinforcement Learning: A Case Study of Russian Trolls in the 2016 US Election
Since the 2016 US Presidential election, social media abuse has been eliciting massive concern in the academic community and beyond.
Learning Behavioral Representations from Wearable Sensors
Continuous collection of physiological data from wearable sensors enables temporal characterization of individual behaviors.
Benchmarks for Graph Embedding Evaluation
We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance.
The History of Digital Spam
: that's what Lorrie Faith Cranor and Brian LaMacchia exclaimed in the title of a popular call-to-action article that appeared twenty years ago on Communications of the ACM.
Discovering Hidden Structure in High Dimensional Human Behavioral Data via Tensor Factorization
In recent years, the rapid growth in technology has increased the opportunity for longitudinal human behavioral studies.
Discovering patterns of online popularity from time series
By clustering the multidimensional time-series of the popularity of contents coupled with other domain-specific dimensions, we uncover two main patterns of popularity: bursty and steady temporal behaviors.
Characterizing Activity on the Deep and Dark Web
The deep and darkweb (d2web) refers to limited access web sites that require registration, authentication, or more complex encryption protocols to access them.
Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior
We studied bot interactions with humans and observed different strategies.
Social and Information Networks
Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms
One of the hallmarks of a free and fair society is the ability to conduct a peaceful and seamless transfer of power from one leader to another.
Social and Information Networks
Arming the public with artificial intelligence to counter social bots
Researchers have responded by developing AI tools to arm the public in the fight against social bots.
Computers and Society
DynamicGEM: A Library for Dynamic Graph Embedding Methods
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.
'Senator, We Sell Ads': Analysis of the 2016 Russian Facebook Ads Campaign
The 2016 US presidential election stands out due to suspected foreign influence before, during, and after the election.
Social and Information Networks
Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction
Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data providing temporal characterization of an individual's behaviors.
Discovering Signals from Web Sources to Predict Cyber Attacks
Cyber attacks are growing in frequency and severity.
Capturing Edge Attributes via Network Embedding
Here we propose a novel embedding method that uses both network structure and edge attributes to learn better network representations.
Social and Information Networks
Predicting Cyber Events by Leveraging Hacker Sentiment
We propose a novel approach to predict cyber events using sentiment analysis.
Deep Neural Networks for Bot Detection
In this paper, we propose a deep neural network based on contextual long short-term memory (LSTM) architecture that exploits both content and metadata to detect bots at the tweet level: contextual features are extracted from user metadata and fed as auxiliary input to LSTM deep nets processing the tweet text.
Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election
Prior interests of disinformation adopters pinpoint to the reasons of the scarce success of this campaign: the users who engaged with MacronLeaks are mostly foreigners with a preexisting interest in alt-right topics and alternative news media, rather than French users with diverse political views.
Social and Information Networks Human-Computer Interaction Physics and Society
Graph Embedding Techniques, Applications, and Performance: A Survey
Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications.
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots.
Social and Information Networks
Non-negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games
Multiplayer online battle arena has become a popular game genre.
Predicting online extremism, content adopters, and interaction reciprocity
We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media.
BotOrNot: A System to Evaluate Social Bots
While most online social media accounts are controlled by humans, these platforms also host automated agents called social bots or sybil accounts.
Social and Information Networks
The DARPA Twitter Bot Challenge
A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes.
Latent Space Model for Multi-Modal Social Data
We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information.
Quantifying the Effect of Sentiment on Information Diffusion in Social Media
This work aims at quantifying the effect of sentiment on information diffusion, to understand: (i) whether positive conversations spread faster and/or broader than negative ones (or vice-versa); (ii) what kind of emotions are more typical of popular conversations on social media; and, (iii) what type of sentiment is expressed in conversations characterized by different temporal dynamics.
Measuring Emotional Contagion in Social Media
We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce.
On predictability of rare events leveraging social media: a machine learning perspective
However, extents and contexts in which such forecasting power can be effectively leveraged are still unverified at least in a systematic way.
Parallel clustering of high-dimensional social media data streams
We introduce Cloud DIKW as an analysis environment supporting scientific discovery through integrated parallel batch and streaming processing, and apply it to one representative domain application: social media data stream clustering.
Distributed, Parallel, and Cluster Computing Databases Social and Information Networks
Clustering memes in social media streams
The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation.
The Rise of Social Bots
The Turing test aimed to recognize the behavior of a human from that of a computer algorithm.
Social and Information Networks Computers and Society Data Analysis, Statistics and Probability Physics and Society
XML Matchers: approaches and challenges
Schema Matching, i. e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years.
Generalized Louvain Method for Community Detection in Large Networks
In this paper we present a novel strategy to discover the community structure of (possibly, large) networks.
Social and Information Networks Physics and Society
