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Found 67 papers, 22 papers with code
Punctuation Restoration using Transformer Models for High-and Low-Resource Languages
In this work, we explore different transformer based models and propose an augmentation strategy for this task, focusing on high-resource (English) and low-resource (Bangla) languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
COVID-19 in Bulgarian Social Media: Factuality, Harmfulness, Propaganda, and Framing
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic.
LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content
To address this gap, this study focuses on developing a specialized LLM, LlamaLens, for analyzing news and social media content in a multilingual context.
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs
Arabic, with its rich diversity of dialects, remains significantly underrepresented in Large Language Models, particularly in dialectal variations.
Native vs Non-Native Language Prompting: A Comparative Analysis
Since prompts play a crucial role in understanding their capabilities, the language used for prompts remains an important research question.
Propaganda to Hate: A Multimodal Analysis of Arabic Memes with Multi-Agent LLMs
In the current literature, there have been efforts to individually detect such content in memes.
NativQA: Multilingual Culturally-Aligned Natural Query for LLMs
Natural Question Answering (QA) datasets play a crucial role in evaluating the capabilities of large language models (LLMs), ensuring their effectiveness in real-world applications.
ArAIEval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Content
We further provide a brief overview of the participating systems.
ThatiAR: Subjectivity Detection in Arabic News Sentences
In this study, we present the first large dataset for subjectivity detection in Arabic, consisting of ~3. 6K manually annotated sentences, and GPT-4o based explanation.
ArMeme: Propagandistic Content in Arabic Memes
Identification of such misleading and persuasive multimodal content has become more important among various stakeholders, including social media platforms, policymakers, and the broader society as they often cause harm to individuals, organizations, and/or society.
Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles
Finally, we evaluate GPT-4 on a dataset consisting of six other languages for span detection, and results suggest that the model struggles with the task across languages.
Large Language Models for Propaganda Span Annotation
Finally, we examine the effectiveness of labels provided by GPT-4 in training smaller language models for the task.
ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text
We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023.
Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection
The spread of disinformation and propagandistic content poses a threat to societal harmony, undermining informed decision-making and trust in reliable sources.
Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition
Our results demonstrate the efficacy of the model trained on psuedo-label data for the designed test-set along with publicly-available Bangla datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
BLP-2023 Task 2: Sentiment Analysis
Additionally, we provide a brief overview of the systems submitted by the participants.
Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis
The rapid expansion of the digital world has propelled sentiment analysis into a critical tool across diverse sectors such as marketing, politics, customer service, and healthcare.
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking
In this study, we introduce the LLMeBench framework, which can be seamlessly customized to evaluate LLMs for any NLP task, regardless of language.
LAraBench: Benchmarking Arabic AI with Large Language Models
Our findings provide valuable insights into the applicability of LLMs for Arabic NLP and speech processing tasks.
Detecting and Reasoning of Deleted Tweets before they are Posted
Social media platforms empower us in several ways, from information dissemination to consumption.
QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models
This paper describes our participating system to this task.
Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic
Thus, there has been a lot of recent research on automatic detection of propaganda techniques in text as well as in memes.
ConceptX: A Framework for Latent Concept Analysis
We use an unsupervised method to discover concepts learned in these models and enable a graphical interface for humans to generate explanations for the concepts.
On the Transformation of Latent Space in Fine-Tuned NLP Models
We study the evolution of latent space in fine-tuned NLP models.
Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet Text
In further experiments, our evaluation shows that transformer models (BERT-m and XLM-RoBERTa-base) outperform the SVM and RF in Dutch and English languages where a different scenario is observed for Spanish.
Analyzing Encoded Concepts in Transformer Language Models
We propose a novel framework ConceptX, to analyze how latent concepts are encoded in representations learned within pre-trained language models.
Discovering Latent Concepts Learned in BERT
We address this limitation by discovering and analyzing latent concepts learned in neural network models in an unsupervised fashion and provide interpretations from the model's perspective.
TeamX@DravidianLangTech-ACL2022: A Comparative Analysis for Troll-Based Meme Classification
The spread of fake news, propaganda, misinformation, disinformation, and harmful content online raised concerns among social media platforms, government agencies, policymakers, and society as a whole.
Detecting and Understanding Harmful Memes: A Survey
One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.
Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations
The content that is posted and shared online can be textual, visual, or a combination of both, e. g., in a meme.
QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media
Fighting the ongoing COVID-19 infodemic has been declared as one of the most important focus areas by the World Health Organization since the onset of the COVID-19 pandemic.
ArabGend: Gender Analysis and Inference on Arabic Twitter
Gender analysis of Twitter can reveal important socio-cultural differences between male and female users.
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination
We studied the data for individual types of tweets and temporal changes in stance towards vaccine.
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions
These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing.
Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News
We describe the fourth edition of the CheckThat!
Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection
Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used.
A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar
While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer fake news may hinder efforts to recover from the first one.
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document
The output is a re-ranked list of the document sentences, so that those that can be verified are ranked as high as possible, together with corresponding evidence.
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
This is the first dataset of its kind: social media images, disaster response, and multi-task learning research.
Detecting Propaganda Techniques in Memes
We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both.
A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models
In this study, we first provide a review of Bangla NLP tasks, resources, and tools available to the research community; we benchmark datasets collected from various platforms for nine NLP tasks using current state-of-the-art algorithms (i. e., transformer-based models).
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems.
The Role of Context in Detecting Previously Fact-Checked Claims
Recent years have seen the proliferation of disinformation and fake news online.
Effect of Post-processing on Contextualized Word Representations
However, post-processing for contextualized embeddings is an under-studied problem.
Robust Training of Social Media Image Classification Models for Rapid Disaster Response
Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks.
HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks
Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters.
Automated Fact-Checking for Assisting Human Fact-Checkers
The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism.
A Survey on Multimodal Disinformation Detection
As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content.
Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset
In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews.
Flood Detection via Twitter Streams using Textual and Visual Features
The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over Twitter.
Sentiment Classification in Bangla Textual Content: A Comparative Study
In this study, we explore several publicly available sentiment labeled datasets and designed classifiers using both classical and deep learning algorithms.
Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response
In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment.
Bangla Text Classification using Transformers
Text classification has been one of the earliest problems in NLP.
Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms
With the outbreak of the COVID-19 pandemic, people turned to social media to read and to share timely information including statistics, warnings, advice, and inspirational stories.
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic.
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings.
CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing
Time-critical analysis of social media streams is important for humanitarian organizations for planing rapid response during disasters.
Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response
Multimedia content in social media platforms provides significant information during disaster events.
Ranked #1 on
Disaster Response
on CrisisMMD
Domain Adaptation with Adversarial Training and Graph Embeddings
In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains.
Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets
During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situational awareness and to plan response efforts.
CrisisMMD: Multimodal Twitter Datasets from Natural Disasters
Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types.
Social and Information Networks Computers and Society
Annotating and Modeling Empathy in Spoken Conversations
The automatic classification system was evaluated on call center conversations where it showed significantly better performance than the baseline.
Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises
The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly.
The Social Mood of News: Self-reported Annotations to Design Automatic Mood Detection Systems
In this paper, we address the issue of automatic prediction of readers{'} mood from newspaper articles and comments.
How Interlocutors Coordinate with each other within Emotional Segments?
In this paper, we aim to investigate the coordination of interlocutors behavior in different emotional segments.
Multilevel Annotation of Agreement and Disagreement in Italian News Blogs
In this paper, we present a corpus of news blog conversations in Italian annotated with gold standard agreement/disagreement relations at message and sentence levels.
Unsupervised recognition and clustering of speech overlaps in spoken conversations
Then, we used unsupervised clustering to find the distinct and well-separated clusters in terms of acoustic and lexical features.
