arXiv subjects

Imran Razzak

Publications and source records attributed to Imran Razzak.

150 records · Page 9Linked to original sources

arXiv2021

Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction

COVID-19 has affected the world economy and the daily life routine of almost everyone. It has been a hot topic on social media platforms such as Twitter, Facebook, etc. These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread. Twitter's retweet functionality allows users to share the existing content with other users without altering the original content. Analysis of social media platforms can help in detecting emergencies during pandemics that lead to taking preventive measures. One such type of analysis is predicting the number of retweets for a given COVID-19 related tweet. Recently, CIKM organized a retweet prediction challenge for COVID-19 tweets focusing on using numeric features only. However, our hypothesis is, tweet text may play a vital role in an accurate retweet prediction. In this paper, we combine numeric and text features for COVID-19 related retweet predictions. For this purpose, we propose two CNN and RNN based models and evaluate the performance of these models on a publicly available TweetsCOV19 dataset using seven different evaluation metrics. Our evaluation results show that combining tweet text with numeric features improves the performance of retweet prediction significantly.

cs.SI↗

arXiv2021

DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media

Twitter is currently a popular online social media platform which allows users to share their user-generated content. This publicly-generated user data is also crucial to healthcare technologies because the discovered patterns would hugely benefit them in several ways. One of the applications is in automatically discovering mental health problems, e.g., depression. Previous studies to automatically detect a depressed user on online social media have largely relied upon the user behaviour and their linguistic patterns including user's social interactions. The downside is that these models are trained on several irrelevant content which might not be crucial towards detecting a depressed user. Besides, these content have a negative impact on the overall efficiency and effectiveness of the model. To overcome the shortcomings in the existing automatic depression detection methods, we propose a novel computational framework for automatic depression detection that initially selects relevant content through a hybrid extractive and abstractive summarization strategy on the sequence of all user tweets leading to a more fine-grained and relevant content. The content then goes to our novel deep learning framework comprising of a unified learning machinery comprising of Convolutional Neural Network (CNN) coupled with attention-enhanced Gated Recurrent Units (GRU) models leading to better empirical performance than existing strong baselines.

cs.LG↗

arXiv2021

Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media

Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction. However, most of the existing machine learning methods provide no interpretability for depression prediction, hence their predictions are obscure to human. In this work, we propose interpretive Multi-Modal Depression Detection with Hierarchical Attention Network MDHAN, for detection depressed users on social media and explain the model prediction. We have considered user posts along with Twitter-based multi-modal features, specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words' importance, and capture semantic sequence features from the user timelines (posts). Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-modal features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.

cs.IR↗

arXiv2020

A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models

Word representation has always been an important research area in the history of natural language processing (NLP). Understanding such complex text data is imperative, given that it is rich in information and can be used widely across various applications. In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS). We describe a variety of text representation methods, and model designs have blossomed in the context of NLP, including SOTA LMs. These models can transform large volumes of text into effective vector representations capturing the same semantic information. Further, such representations can be utilized by various machine learning (ML) algorithms for a variety of NLP related tasks. In the end, this survey briefly discusses the commonly used ML and DL based classifiers, evaluation metrics and the applications of these word embeddings in different NLP tasks.

cs.CL↗

arXiv2020

BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition

In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially. However, BioNER research is challenging as NER in the biomedical domain are: (i) often restricted due to limited amount of training data, (ii) an entity can refer to multiple types and concepts depending on its context and, (iii) heavy reliance on acronyms that are sub-domain specific. Existing BioNER approaches often neglect these issues and directly adopt the state-of-the-art (SOTA) models trained in general corpora which often yields unsatisfactory results. We propose biomedical ALBERT (A Lite Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) bioALBERT, an effective domain-specific language model trained on large-scale biomedical corpora designed to capture biomedical context-dependent NER. We adopted a self-supervised loss used in ALBERT that focuses on modelling inter-sentence coherence to better learn context-dependent representations and incorporated parameter reduction techniques to lower memory consumption and increase the training speed in BioNER. In our experiments, BioALBERT outperformed comparative SOTA BioNER models on eight biomedical NER benchmark datasets with four different entity types. We trained four different variants of BioALBERT models which are available for the research community to be used in future research.

cs.CL↗

arXiv2020

Leveraging Big Data Analytics in Healthcare Enhancement: Trends, Challenges and Opportunities

Clinicians decisions are becoming more and more evidence-based meaning in no other field the big data analytics so promising as in healthcare. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate and manage large amount of healthcare data either in structured or unstructured form. In this paper, we would like to address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare i.e.medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We presents different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare.

stat.OT↗