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Found 24 papers, 4 papers with code
Analyzing Toxicity in Deep Conversations: A Reddit Case Study
This anonymity has also made social media prone to harmful content, which requires moderation to ensure responsible and productive use.
SONIC: Synergizing VisiON Foundation Models for Stress RecogNItion from ECG signals
Stress recognition through physiological signals such as Electrocardiogram (ECG) signals has garnered significant attention.
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake
To validate our hypothesis, we extract representations from state-of-the-art (SOTA) PTMs including monolingual, multilingual as well as PTMs trained for speaker and emotion recognition, and evaluated them on ASVSpoof 2019 (ASV), In-the-Wild (ITW), and DECRO benchmark databases.
Revisiting The Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems
To summarize, this work highlights the pervasive nature of gender stereotypes in literary works and reveals the potential of LLMs to rectify gender stereotypes.
How Paralingual are Paralinguistic Representations? A Case Study in Speech Emotion Recognition
We also show that downstream models using TRILLsson representations achieve SOTA performance in terms of accuracy across various multi-lingual datasets.
A Survey on Online User Aggression: Content Detection and Behavioural Analysis on Social Media Platforms
Recognizing the societal risks associated with unchecked aggressive content, this paper delves into the field of Aggression Content Detection and Behavioral Analysis of Aggressive Users, aiming to bridge the gap between disparate studies.
AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System
While the ideas herein can be generalized and reapplied in the broader context of misinformation mitigation using a multitude of information sources and catering to the spectrum of social media platforms, this work serves as a proof of concept, and as such, it is confined in its scope to only rebuttal of tweets, and in the specific context of misinformation regarding COVID-19.
Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking
Extensive experiments on FB15K-277 and NELL-995 datasets reveal that reasoning over a KG is an effective way of producing human-readable explanations in the form of paths and classifications for fact claims.
Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination
We demonstrate the Misinformation Concierge, a proof-of-concept that provides actionable intelligence on misinformation prevalent in social media.
Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks
Speech emotion recognition (SER) is a field that has drawn a lot of attention due to its applications in diverse fields.
minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Lattices
For a given set of Hate Terms lists (HTs-lists) and Hate Speech data (HS-data), it is challenging to understand which hate term contributes the most for hate speech classification.
A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition
In this work, we exploit this research gap and perform a comparative analysis of embeddings extracted from eight speech and audio PTMs (wav2vec 2. 0, data2vec, wavLM, UniSpeech-SAT, wav2clip, YAMNet, x-vector, ECAPA).
Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France
Nonetheless, alternative data sources, such as call data records (CDR) and mobile app usage, can serve as cost-effective and up-to-date sources for identifying socio-economic indicators.
GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection
A plethora of previous multimodal-based work has tried to address the problem of modeling heterogeneous modalities in identifying fake content.
Pre-Trained Language Transformers are Universal Image Classifiers
The pretrained transformer of GPT-2 is trained to generate text and then fine-tuned to classify facial images.
What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses
In the descriptive line of works, where researchers have tried to analyse rumours using NLP approaches, there isnt much emphasis on psycho-linguistics analyses of social media text.
Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic
We build on this to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones.
DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection
Our approach is a combination of the NLP -- where we encode the news content, and the GNN technique -- where we encode the Knowledge Graph (KG).
Misinformation Detection on YouTube Using Video Captions
Using caption dataset, the proposed models can classify videos among three classes (Misinformation, Debunking Misinformation, and Neutral) with 0. 85 to 0. 90 F1-score.
A Graph Convolutional Neural Network based Framework for Estimating Future Citations Count of Research Articles
Scientific publications play a vital role in the career of a researcher.
COVID-19 and the stock market: evidence from Twitter
COVID-19 has had a much larger impact on the financial markets compared to previous epidemics because the news information is transferred over the social networks at a speed of light.
Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach
Thus, it is important to detect and control the misinformation in such platforms before it spreads to the masses.
Which bills are lobbied? Predicting and interpreting lobbying activity in the US
We also investigate the influence of the intensity of the lobbying activity on how discernible a lobbied bill is from one that was not subject to lobbying.
Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study
Three out of four proposed architectures outperformed the accuracy from previous machine learning and deep learning research work, two out of four models outperformed accuracy from previous deep learning study on Quora's question pair dataset, and our best model achieved accuracy of 85. 82% which is close to Quora state of the art accuracy.
