arXiv subjects

Ponnurangam Kumaraguru

Publications and source records attributed to Ponnurangam Kumaraguru.

At least 109 records · Page 6Linked to original sources

arXiv2021

(Un)Masked COVID-19 Trends from Social Media

Wearing masks is a useful protection method against COVID-19, which has caused widespread economic and social impact worldwide. Across the globe, governments have put mandates for the use of face masks, which have received both positive and negative reaction. Online social media provides an exciting platform to study the use of masks and analyze underlying mask-wearing patterns. In this article, we analyze 2.04 million social media images for six US cities. An increase in masks worn in images is seen as the COVID-19 cases rose, particularly when their respective states imposed strict regulations. We also found a decrease in the posting of group pictures as stay-at-home laws were put into place. Furthermore, mask compliance in the Black Lives Matter protest was analyzed, eliciting that 40% of the people in group photos wore masks, and 45% of them wore the masks with a fit score of greater than 80%. We introduce two new datasets, VAriety MAsks - Classification (VAMA-C) and VAriety MAsks - Segmentation (VAMA-S), for mask detection and mask fit analysis tasks, respectively. For the analysis, we create two frameworks, face mask detector (for classifying masked and unmasked faces) and mask fit analyzer (a semantic segmentation based model to calculate a mask-fit score). The face mask detector achieved a classification accuracy of 98%, and the semantic segmentation model for the mask fit analyzer achieved an Intersection Over Union (IOU) score of 98%. We conclude that such a framework can be used to evaluate the effectiveness of such public health strategies using social media platforms in times of pandemic.

cs.CV↗

arXiv2021

"Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning

The exponential rise of online social media has enabled the creation, distribution, and consumption of information at an unprecedented rate. However, it has also led to the burgeoning of various forms of online abuse. Increasing cases of online antisemitism have become one of the major concerns because of its socio-political consequences. Unlike other major forms of online abuse like racism, sexism, etc., online antisemitism has not been studied much from a machine learning perspective. To the best of our knowledge, we present the first work in the direction of automated multimodal detection of online antisemitism. The task poses multiple challenges that include extracting signals across multiple modalities, contextual references, and handling multiple aspects of antisemitism. Unfortunately, there does not exist any publicly available benchmark corpus for this critical task. Hence, we collect and label two datasets with 3,102 and 3,509 social media posts from Twitter and Gab respectively. Further, we present a multimodal deep learning system that detects the presence of antisemitic content and its specific antisemitism category using text and images from posts. We perform an extensive set of experiments on the two datasets to evaluate the efficacy of the proposed system. Finally, we also present a qualitative analysis of our study.

cs.MM↗

arXiv2021

Factorization of Fact-Checks for Low Resource Indian Languages

The advancement in technology and accessibility of internet to each individual is revolutionizing the real time information. The liberty to express your thoughts without passing through any credibility check is leading to dissemination of fake content in the ecosystem. It can have disastrous effects on both individuals and society as a whole. The amplification of fake news is becoming rampant in India too. Debunked information often gets republished with a replacement description, claiming it to depict some different incidence. To curb such fabricated stories, it is necessary to investigate such deduplicates and false claims made in public. The majority of studies on automatic fact-checking and fake news detection is restricted to English only. But for a country like India where only 10% of the literate population speak English, role of regional languages in spreading falsity cannot be undermined. In this paper, we introduce FactDRIL: the first large scale multilingual Fact-checking Dataset for Regional Indian Languages. We collect an exhaustive dataset across 7 months covering 11 low-resource languages. Our propose dataset consists of 9,058 samples belonging to English, 5,155 samples to Hindi and remaining 8,222 samples are distributed across various regional languages, i.e. Bangla, Marathi, Malayalam, Telugu, Tamil, Oriya, Assamese, Punjabi, Urdu, Sinhala and Burmese. We also present the detailed characterization of three M's (multi-lingual, multi-media, multi-domain) in the FactDRIL accompanied with the complete list of other varied attributes making it a unique dataset to study. Lastly, we present some potential use cases of the dataset. We expect this dataset will be a valuable resource and serve as a starting point to fight proliferation of fake news in low resource languages.

cs.CL↗

arXiv2021

Capitol (Pat)riots: A comparative study of Twitter and Parler

On 6 January 2021, a mob of right-wing conservatives stormed the USA Capitol Hill interrupting the session of congress certifying 2020 Presidential election results. Immediately after the start of the event, posts related to the riots started to trend on social media. A social media platform which stood out was a free speech endorsing social media platform Parler; it is being claimed as the platform on which the riots were planned and talked about. Our report presents a contrast between the trending content on Parler and Twitter around the time of riots. We collected data from both platforms based on the trending hashtags and draw comparisons based on what are the topics being talked about, who are the people active on the platforms and how organic is the content generated on the two platforms. While the content trending on Twitter had strong resentments towards the event and called for action against rioters and inciters, Parler content had a strong conservative narrative echoing the ideas of voter fraud similar to the attacking mob. We also find a disproportionately high manipulation of traffic on Parler when compared to Twitter.

cs.CY↗

arXiv2020

Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers

In the era of MOOCs, online exams are taken by millions of candidates, where scoring short answers is an integral part. It becomes intractable to evaluate them by human graders. Thus, a generic automated system capable of grading these responses should be designed and deployed. In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS). We propose and explain the design and development of a system for SAS, namely AutoSAS. Given a question along with its graded samples, AutoSAS can learn to grade that prompt successfully. This paper further lays down the features such as lexical diversity, Word2Vec, prompt, and content overlap that plays a pivotal role in building our proposed model. We also present a methodology for indicating the factors responsible for scoring an answer. The trained model is evaluated on an extensively used public dataset, namely Automated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSAS shows state-of-the-art performance and achieves better results by over 8% in some of the question prompts as measured by Quadratic Weighted Kappa (QWK), showing performance comparable to humans.

cs.AI↗

arXiv2020

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic

Background: The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this Infodemic requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. Objective: We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. Methods: We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. Results: A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot Satya increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. Conclusion: We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation.

cs.CY↗

arXiv2020

AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts

While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate speech, offensive language, sexist and racist opinions, etc. Detection and curtailment of such abusive content is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes. Previous works have focused on classifying user posts into various forms of abusive behavior. But there has hardly been any focus on estimating the severity of abuse and the target. In this paper, we present a first of the kind dataset with 7601 posts from Gab which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior. We also propose a system to address these tasks, obtaining an accuracy of ~80% for abuse presence, ~82% for abuse target prediction, and ~65% for abuse severity prediction.

cs.CL↗

arXiv2020

imdpGAN: Generating Private and Specific Data with Generative Adversarial Networks

Generative Adversarial Network (GAN) and its variants have shown promising results in generating synthetic data. However, the issues with GANs are: (i) the learning happens around the training samples and the model often ends up remembering them, consequently, compromising the privacy of individual samples - this becomes a major concern when GANs are applied to training data including personally identifiable information, (ii) the randomness in generated data - there is no control over the specificity of generated samples. To address these issues, we propose imdpGAN - an information maximizing differentially private Generative Adversarial Network. It is an end-to-end framework that simultaneously achieves privacy protection and learns latent representations. With experiments on MNIST dataset, we show that imdpGAN preserves the privacy of the individual data point, and learns latent codes to control the specificity of the generated samples. We perform binary classification on digit pairs to show the utility versus privacy trade-off. The classification accuracy decreases as we increase privacy levels in the framework. We also experimentally show that the training process of imdpGAN is stable but experience a 10-fold time increase as compared with other GAN frameworks. Finally, we extend imdpGAN framework to CelebA dataset to show how the privacy and learned representations can be used to control the specificity of the output.

cs.CV↗

arXiv2020

Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments

Smart homes require every device inside them to be connected with each other at all times, which leads to a lot of power wastage on a daily basis. As the devices inside a smart home increase, it becomes difficult for the user to control or operate every individual device optimally. Therefore, users generally rely on power management systems for such optimization but often are not satisfied with the results. In this paper, we present a novel multi-objective reinforcement learning framework with two-fold objectives of minimizing power consumption and maximizing user satisfaction. The framework explores the trade-off between the two objectives and converges to a better power management policy when both objectives are considered while finding an optimal policy. We experiment on real-world smart home data, and show that the multi-objective approaches: i) establish trade-off between the two objectives, ii) achieve better combined user satisfaction and power consumption than single-objective approaches. We also show that the devices that are used regularly and have several fluctuations in device modes at regular intervals should be targeted for optimization, and the experiments on data from other smart homes fetch similar results, hence ensuring transfer-ability of the proposed framework.

cs.AI↗

arXiv2020

Trawling for Trolling: A Dataset

The ability to accurately detect and filter offensive content automatically is important to ensure a rich and diverse digital discourse. Trolling is a type of hurtful or offensive content that is prevalent in social media, but is underrepresented in datasets for offensive content detection. In this work, we present a dataset that models trolling as a subcategory of offensive content. The dataset was created by collecting samples from well-known datasets and reannotating them along precise definitions of different categories of offensive content. The dataset has 12,490 samples, split across 5 classes; Normal, Profanity, Trolling, Derogatory and Hate Speech. It encompasses content from Twitter, Reddit and Wikipedia Talk Pages. Models trained on our dataset show appreciable performance without any significant hyperparameter tuning and can potentially learn meaningful linguistic information effectively. We find that these models are sensitive to data ablation which suggests that the dataset is largely devoid of spurious statistical artefacts that could otherwise distract and confuse classification models.

cs.CY↗

arXiv2020

WorkerRep: Immutable Reputation System For Crowdsourcing Platform Based on Blockchain

Crowdsourcing is a process wherein an individual or an organisation utilizes the talent pool present over the Internet to accomplish their task. The existing crowdsourcing platforms and their reputation computation are centralised and hence prone to various attacks or malicious manipulation of the data by the central entity. A few distributed crowdsourcing platforms have been proposed but they lack a robust reputation mechanism. So we propose a decentralised crowdsourcing platform having an immutable reputation mechanism to tackle these problems. It is built on top of Ethereum network and does not require the user to trust a third party for a non malicious experience. It also utilizes IOTAs consensus mechanism which reduces the cost for task evaluation significantly.

cs.CR↗

arXiv2020

Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India during COVID-19 Infodemic

COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people's health and governance systems. Since social media is the largest source of information, managing the infodemic not only requires mitigating of misinformation but also an early understanding of psychological patterns resulting from it. During the COVID-19 crisis, Twitter alone has seen a sharp 45% increase in the usage of its curated events page, and a 30% increase in its direct messaging usage, since March 6th 2020. In this study, we analyze the psychometric impact and coupling of the COVID-19 infodemic with the official bulletins related to COVID-19 at the national and state level in India. We look at these two sources with a psycho-linguistic lens of emotions and quantified the extent and coupling between the two. We modified path, a deep skip-gram based open-sourced lexicon builder for effective capture of health-related emotions. We were then able to capture the time-evolution of health-related emotions in social media and official bulletins. An analysis of lead-lag relationships between the time series of extracted emotions from official bulletins and social media using Granger's causality showed that state bulletins were leading the social media for some emotions such as Medical Emergency. Further insights that are potentially relevant for the policymaker and the communicators actively engaged in mitigating misinformation are also discussed. Our paper also introduces CoronaIndiaDataset2, the first social media based COVID-19 dataset at national and state levels from India with over 5.6 million national and 2.6 million state-level tweets. Finally, we present our findings as COVibes, an interactive web application capturing psychometric insights captured upon the CoronaIndiaDataset, both at a national and state level.

cs.CL↗

arXiv2020

Hashtags are (not) judgemental: The untold story of Lok Sabha elections 2019

Hashtags in online social media have become a way for users to build communities around topics, promote opinions, and categorize messages. In the political context, hashtags on Twitter are used by users to campaign for their parties, spread news, or to get followers and get a general idea by following a discussion built around a hashtag. In the past, researchers have studied certain types and specific properties of hashtags by utilizing a lot of data collected around hashtags. In this paper, we perform a large-scale empirical analysis of elections using only the hashtags shared on Twitter during the 2019 Lok Sabha elections in India. We study the trends and events unfolded on the ground, the latent topics to uncover representative hashtags and semantic similarity to relate hashtags with the election outcomes. We collect over 24 million hashtags to perform extensive experiments. First, we find the trending hashtags to cross-reference them with the tweets in our dataset to list down notable events. Second, we use Latent Dirichlet Allocation to find topic patterns in the dataset. In the end, we use skip-gram word embedding model to find semantically similar hashtags. We propose popularity and an influence metric to predict election outcomes using just the hashtags. Empirical results show that influence is a good measure to predict the election outcome.

cs.SI↗

arXiv2020

Modeling Citation Trajectories of Scientific Papers

Several network growth models have been proposed in the literature that attempt to incorporate properties of citation networks. Generally, these models aim at retaining the degree distribution observed in real-world networks. In this work, we explore whether existing network growth models can realize the diversity in citation growth exhibited by individual papers - a new node-centric property observed recently in citation networks across multiple domains of research. We theoretically and empirically show that the network growth models which are solely based on degree and/or intrinsic fitness cannot realize certain temporal growth behaviors that are observed in real-world citation networks. To this end, we propose two new growth models that localize the influence of papers through an appropriate attachment mechanism. Experimental results on the real-world citation networks of Computer Science and Physics domains show that our proposed models can better explain the temporal behavior of citation networks than existing models.

cs.SI↗

arXiv2020

Don't cross that stop line: Characterizing Traffic Violations in Metropolitan Cities

In modern metropolitan cities, the task of ensuring safe roads is of paramount importance. Automated systems of e-challans (Electronic traffic-violation receipt) are now being deployed across cities to record traffic violations and to issue fines. In the present study, an automated e-challan system established in Ahmedabad (Gujarat, India) has been analyzed for characterizing user behaviour, violation types as well as finding spatial and temporal patterns in the data. We describe a method of collecting e-challan data from the e-challan portal of Ahmedabad traffic police and create a dataset of over 3 million e-challans. The dataset was first analyzed to characterize user behaviour with respect to repeat offenses and fine payment. We demonstrate that a lot of users repeat their offenses (traffic violation) frequently and are less likely to pay fines of higher value. Next, we analyze the data from a spatial and temporal perspective and identify certain spatio-temporal patterns present in our dataset. We find that there is a drastic increase/decrease in the number of e-challans issued during the festival days and identify a few hotspots in the city that have high intensity of traffic violations. In the end, we propose a set of 5 features to model recidivism in traffic violations and train multiple classifiers on our dataset to evaluate the effectiveness of our proposed features. The proposed approach achieves 95% accuracy on the dataset.

cs.CY↗

arXiv2019

Driving The Last Mile: Characterizing and Understanding Distracted Driving Posts on Social Networks

In 2015, 391,000 people were injured due to distracted driving in the US. One of the major reasons behind distracted driving is the use of cell-phones, accounting for 14% of fatal crashes. Social media applications have enabled users to stay connected, however, the use of such applications while driving could have serious repercussions -- often leading the user to be distracted from the road and ending up in an accident. In the context of impression management, it has been discovered that individuals often take a risk (such as teens smoking cigarettes, indulging in narcotics, and participating in unsafe sex) to improve their social standing. Therefore, viewing the phenomena of posting distracted driving posts under the lens of self-presentation, it can be hypothesized that users often indulge in risk-taking behavior on social media to improve their impression among their peers. In this paper, we first try to understand the severity of such social-media-based distractions by analyzing the content posted on a popular social media site where the user is driving and is also simultaneously creating content. To this end, we build a deep learning classifier to identify publicly posted content on social media that involves the user driving. Furthermore, a framework proposed to understand factors behind voluntary risk-taking activity observes that younger individuals are more willing to perform such activities, and men (as opposed to women) are more inclined to take risks. Grounding our observations in this framework, we test these hypotheses on 173 cities across the world. We conduct spatial and temporal analysis on a city-level and understand how distracted driving content posting behavior changes due to varied demographics. We discover that the factors put forth by the framework are significant in estimating the extent of such behavior.

cs.SI↗

arXiv2019

Is change the only constant? Profile change perspective on #LokSabhaElections2019

Users on Twitter are identified with the help of their profile attributes that consists of username, display name, profile image, to name a few. The profile attributes that users adopt can reflect their interests, belief, or thematic inclinations. Literature has proposed the implications and significance of profile attribute change for a random population of users. However, the use of profile attribute for endorsements and to start a movement have been under-explored. In this work, we consider #LokSabhaElections2019 as a movement and perform a large-scale study of the profile of users who actively made changes to profile attributes centered around #LokSabhaElections2019. We collect the profile metadata for 49.4M users for a period of 2 months from April 5, 2019 to June 5, 2019 amid #LokSabhaElections2019. We investigate how the profile changes vary for the influential leaders and their followers over the social movement. We further differentiate the organic and inorganic ways to show the political inclination from the prism of profile changes. We report how the addition of election campaign related keywords lead to spread of behavior contagion and further investigate it with respect to "Chowkidar Movement" in detail.

cs.CL↗

arXiv2019

Catching up with trends: The changing landscape of political discussions on twitter in 2014 and 2019

The advent of 4G increased the usage of internet in India, which took a huge number of discussions online. Online Social Networks (OSNs) are the center of these discussions. During elections, political discussions constitute a significant portion of the trending topics on these networks. Politicians and political parties catch up with these trends, and social media then becomes a part of their publicity agenda. We cannot ignore this trend in any election, be it the U.S, Germany, France, or India. Twitter is a major platform where we observe these trends. In this work, we examine the magnitude of political discussions on twitter by contrasting the platform usage on levels like gender, political party, and geography, in 2014 and 2019 Indian General Elections. In a further attempt to understand the strategies followed by political parties, we compare twitter usage by Bharatiya Janata Party (BJP) and Indian National Congress (INC) in 2019 General Elections in terms of how efficiently they make use of the platform. We specifically analyze the handles of politicians who emerged victorious. We then proceed to compare political handles held by frontmen of BJP and INC: Narendra Modi (@narendramodi) and Rahul Gandhi (@RahulGandhi) using parameters like "following", "tweeting habits", "sources used to tweet", along with text analysis of tweets. With this work, we also introduce a rich dataset covering a majority of tweets made during the election period in 2014 and 2019.

cs.SI↗