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Found 36 papers, 19 papers with code
Mysterious Projections: Multimodal LLMs Gain Domain-Specific Visual Capabilities Without Richer Cross-Modal Projections
It is desirable to understand the roles of these two modules in modeling domain-specific visual attributes to inform the design of future models and streamline the interpretability efforts on the current models.
Towards Fair Graph Anomaly Detection: Problem, New Datasets, and Evaluation
The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while ensuring fairness and avoiding biased predictions against individuals from sensitive subgroups such as gender or political leanings.
MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms
Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces.
FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning
Modern recommender systems may output considerably different recommendations due to small perturbations in the training data.
Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries
Our findings underscore the pressing need to bolster the cross-lingual capacities of these models, and to provide an equitable information ecosystem accessible to all.
Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes
In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23.
Findings of Factify 2: Multimodal Fake News Detection
With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent.
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding
Our results on six GLUE tasks indicate that compared to fully fine-tuned models, vanilla FSL methods lead to a notable relative drop in task performance (i. e., are less robust) in the face of adversarial perturbations.
Cross-Modal Attribute Insertions for Assessing the Robustness of Vision-and-Language Learning
The robustness of multimodal deep learning models to realistic changes in the input text is critical for their applicability to important tasks such as text-to-image retrieval and cross-modal entailment.
Factify 2: A Multimodal Fake News and Satire News Dataset
In this paper, we provide a multi-modal fact-checking dataset called FACTIFY 2, improving Factify 1 by using new data sources and adding satire articles.
Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes
Memes are the new-age conveyance mechanism for humor on social media sites.
Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation
In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students.
A Survey of Graph Neural Networks for Social Recommender Systems
In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis).
Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions
Our work aims to highlight and encourage further research on the robustness of deep multimodal models to realistic variations, especially in human-facing societal applications.
M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations
Using one public and one large industrial dataset, we experimentally show that state-of-the-art SBRSs have low performance on sparse sessions with sparse items.
Implicit Session Contexts for Next-Item Recommendations
Session-based recommender systems capture the short-term interest of a user within a session.
Representation Learning in Continuous-Time Dynamic Signed Networks
Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i. e., link signs and signed weights) in the future.
Overcoming Language Disparity in Online Content Classification with Multimodal Learning
Advances in Natural Language Processing (NLP) have revolutionized the way researchers and practitioners address crucial societal problems.
Characterizing, Detecting, and Predicting Online Ban Evasion
We find that evasion child accounts demonstrate similarities with respect to their banned parent accounts on several behavioral axes - from similarity in usernames and edited pages to similarity in content added to the platform and its psycholinguistic attributes.
Rank List Sensitivity of Recommender Systems to Interaction Perturbations
We introduce a measure of stability for recommender systems, called Rank List Sensitivity (RLS), which measures how rank lists generated by a given recommender system at test time change as a result of a perturbation in the training data.
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models
Here we create a novel adversarial attack model against deep user sequence embedding based classification models, which use the sequence of user posts to generate user embeddings and detect malicious users.
Influence-guided Data Augmentation for Neural Tensor Completion
In this paper, we propose DAIN, a general data augmentation framework that enhances the prediction accuracy of neural tensor completion methods.
The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic
Concerned citizens (i. e., the crowd), who are users of the platforms where misinformation appears, can play a crucial role in disseminating fact-checking information and in countering the spread of misinformation.
Evaluating Graph Vulnerability and Robustness using TIGER
By democratizing the tools required to study network robustness, our goal is to assist researchers and practitioners in analyzing their own networks; and facilitate the development of new research in the field.
M2P2: Multimodal Persuasion Prediction using Adaptive Fusion
Our M2P2 (Multimodal Persuasion Prediction) framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem.
Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis
The spread of COVID-19 has sparked racism and hate on social media targeted towards Asian communities.
Higher-Order Label Homogeneity and Spreading in Graphs
Vertices with stronger connections participate in higher-order structures in graphs, which calls for methods that can leverage these structures in the semi-supervised learning tasks.
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
However, existing dynamic embedding methods generate embeddings only when users take actions and do not explicitly model the future trajectory of the user/item in the embedding space.
Learning Dynamic Embeddings from Temporal Interactions
Here we present JODIE, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions.
False Information on Web and Social Media: A Survey
False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact.
Community Interaction and Conflict on the Web
Here we study intercommunity interactions across 36, 000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community.
FairJudge: Trustworthy User Prediction in Rating Platforms
We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product.
An Army of Me: Sockpuppets in Online Discussion Communities
Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users.
Citation Classification for Behavioral Analysis of a Scientific Field
Citations are an important indicator of the state of a scientific field, reflecting how authors frame their work, and influencing uptake by future scholars.
Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game
Interpersonal relations are fickle, with close friendships often dissolving into enmity.
