mudasirahmadwani Goto Github PK

-mcc-based-suspicious-link-detection-system

Dataset and source code used in article "Mutual Clustering Coefficient-based Suspicious-link Detection Approach for Online Social Networks "

depression-detection

Depression is one of the most common mental disorders with millions of people suffering from it.It has been found to have an impact on the texts written by the affected masses.In this study our main aim was to utilise tweets to predict the possibility of a user at-risk of depression through the use of Natural Language Processing(NLP) tools and deep learning algorithms.LSTM has been used as a baseline model that resulted in an accuracy of 95.12% and an F1 score of 0.9436. We implemented a hybrid Bi-LSTM + CNN model which we trained on learned embeddings from the tweet dataset was able to improve upon previous works and produce precision and recall of 0.9943 and 0.9988 respectively,giving an F1 score of 0.9971.

emotion-based-gender-prediction-in-osns

This project aims to investigate the potential of emotion-based features in the gender identification task, which has been unnoticed by researchers so far. The experimental study is carried out on the texts of two widely used OSNs, Facebook and Twitter.

facebook-dataset-mudasir-

imcrawler

Obtaining the desired dataset is still a prime challenge faced by researchers while analyzing Online Social Network (OSN) sites. Application Programming Interfaces (APIs) provided by OSN service providers for retrieving data impose several unavoidable restrictions which make it difficult to get a desirable dataset. In this paper, we present an iMacros technology-based data crawler called IMcrawler, capable of collecting every piece of information which is accessible through a browser from the Facebook website within the legal framework which permits access to publicly shared user content on OSNs. The proposed crawler addresses most of the challenges allied with web data extraction approaches and most of the APIs provided by OSN service providers. Two broad sections have been extracted from Facebook user profiles, namely, Personal Information and Wall Activities. The present work is the first attempt towards providing the detailed description of crawler design for the Facebook website.

instagram_fake_accounts

moodbook

MoodBook is an emotion dictionary based on the eight basic emotions including fear, anger, sad, joy, disgust, surprise, trust, and anticipation.

mudasir-iiit-bangalore

Config files for my GitHub profile.

mudasirahmadwani.github.io

toxic_fake-news-detection-in-osn

This study ia an attempt to detect toxic fake news on social media and thereby reduce the amount of time spent examining all the categories of fake news which are not toxic.

user-emotion-analysis-in-conflicting-verses-non-conflicting-regions-using-online-social-networks

In this study, we focus on the eight basic emotions, namely fear, anger, sadness, joy, surprise, disgust, trust, and anticipation proposed by Pultchik [12]. We designed and implemented our own lexicon by extending one of the well-known lexicons, namely EmoLex[13] by introducing new mood words extracted from the user content. The posts of 100 users from each region (Delhi and Kashmir) have been analyzed in order to add the most frequent mood words to the new dictionary.