arXiv subjects

Ponnurangam Kumaraguru

Publications and source records attributed to Ponnurangam Kumaraguru.

At least 127 records · Page 7Linked to original sources

arXiv2019

Elites Tweet? Characterizing the Twitter Verified User Network

Social network and publishing platforms, such as Twitter, support the concept of verification. Verified accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been repeated assertions by these platforms about verification not being tantamount to endorsement. However, a significant body of prior work suggests that possessing a verified status symbolizes enhanced credibility in the eyes of the platform audience. As a result, such a status is highly coveted among public figures and influencers. Hence, we attempt to characterize the network of verified users on Twitter and compare the results to similar analysis performed for the entire Twitter network. We extracted the entire network of verified users on Twitter (as of July 2018) and obtained 231,246 user profiles and 79,213,811 connections. Subsequently in the network analysis, we found that the sub-graph of verified users mirrors the full Twitter users graph in some aspects such as possessing a short diameter. However, our findings contrast with earlier findings on multiple aspects, such as the possession of a power law out-degree distribution, slight dissortativity and a significantly higher reciprocity rate, as elucidated in the paper. Moreover, we attempt to gauge the presence of salient components within this sub-graph and detect the absence of homophily with respect to popularity, which again is in stark contrast to the full Twitter graph. Finally, we demonstrate stationarity in the time series of verified user activity levels. To the best of our knowledge, this work represents the first quantitative attempt at characterizing verified users on Twitter.

cs.SI↗

arXiv2019

What sets Verified Users apart? Insights, Analysis and Prediction of Verified Users on Twitter

Social network and publishing platforms, such as Twitter, support the concept of a secret proprietary verification process, for handles they deem worthy of platform-wide public interest. In line with significant prior work which suggests that possessing such a status symbolizes enhanced credibility in the eyes of the platform audience, a verified badge is clearly coveted among public figures and brands. What are less obvious are the inner workings of the verification process and what being verified represents. This lack of clarity, coupled with the flak that Twitter received by extending aforementioned status to political extremists in 2017, backed Twitter into publicly admitting that the process and what the status represented needed to be rethought. With this in mind, we seek to unravel the aspects of a user's profile which likely engender or preclude verification. The aim of the paper is two-fold: First, we test if discerning the verification status of a handle from profile metadata and content features is feasible. Second, we unravel the features which have the greatest bearing on a handle's verification status. We collected a dataset consisting of profile metadata of all 231,235 verified English-speaking users (as of July 2018), a control sample of 175,930 non-verified English-speaking users and all their 494 million tweets over a one year collection period. Our proposed models are able to reliably identify verification status (Area under curve AUC > 99%). We show that number of public list memberships, presence of neutral sentiment in tweets and an authoritative language style are the most pertinent predictors of verification status. To the best of our knowledge, this work represents the first attempt at discerning and classifying verification worthy users on Twitter.

cs.SI↗

arXiv2019

Attentional Road Safety Networks

Road safety mapping using satellite images is a cost-effective but a challenging problem for smart city planning. The scarcity of labeled data, misalignment and ambiguity makes it hard for supervised deep networks to learn efficient embeddings in order to classify between safe and dangerous road segments. In this paper, we address the challenges using a region guided attention network. In our model, we extract global features from a base network and augment it with local features obtained using the region guided attention network. In addition, we perform domain adaptation for unlabeled target data. In order to bridge the gap between safe samples and dangerous samples from source and target respectively, we propose a loss function based on within and between class covariance matrices. We conduct experiments on a public dataset of London to show that the algorithm achieves significant results with the classification accuracy of 86.21%. We obtain an increase of 4% accuracy for NYC using domain adaptation network. Besides, we perform a user study and demonstrate that our proposed algorithm achieves 23.12% better accuracy compared to subjective analysis.

cs.CV↗

arXiv2018

Hardening Deep Neural Networks via Adversarial Model Cascades

Deep neural networks (DNNs) are vulnerable to malicious inputs crafted by an adversary to produce erroneous outputs. Works on securing neural networks against adversarial examples achieve high empirical robustness on simple datasets such as MNIST. However, these techniques are inadequate when empirically tested on complex data sets such as CIFAR-10 and SVHN. Further, existing techniques are designed to target specific attacks and fail to generalize across attacks. We propose the Adversarial Model Cascades (AMC) as a way to tackle the above inadequacies. Our approach trains a cascade of models sequentially where each model is optimized to be robust towards a mixture of multiple attacks. Ultimately, it yields a single model which is secure against a wide range of attacks; namely FGSM, Elastic, Virtual Adversarial Perturbations and Madry. On an average, AMC increases the model's empirical robustness against various attacks simultaneously, by a significant margin (of 6.225% for MNIST, 5.075% for SVHN and 2.65% for CIFAR10). At the same time, the model's performance on non-adversarial inputs is comparable to the state-of-the-art models.

cs.LG↗

arXiv2018

Mind Your Language: Abuse and Offense Detection for Code-Switched Languages

In multilingual societies like the Indian subcontinent, use of code-switched languages is much popular and convenient for the users. In this paper, we study offense and abuse detection in the code-switched pair of Hindi and English (i.e. Hinglish), the pair that is the most spoken. The task is made difficult due to non-fixed grammar, vocabulary, semantics and spellings of Hinglish language. We apply transfer learning and make a LSTM based model for hate speech classification. This model surpasses the performance shown by the current best models to establish itself as the state-of-the-art in the unexplored domain of Hinglish offensive text classification.We also release our model and the embeddings trained for research purposes

cs.CL↗

arXiv2018

IceBreaker: Solving Cold Start Problem for Video Recommendation Engines

Internet has brought about a tremendous increase in content of all forms and, in that, video content constitutes the major backbone of the total content being published as well as watched. Thus it becomes imperative for video recommendation engines such as Hulu to look for novel and innovative ways to recommend the newly added videos to their users. However, the problem with new videos is that they lack any sort of metadata and user interaction so as to be able to rate the videos for the consumers. To this effect, this paper introduces the several techniques we develop for the Content Based Video Relevance Prediction (CBVRP) Challenge being hosted by Hulu for the ACM Multimedia Conference 2018. We employ different architectures on the CBVRP dataset to make use of the provided frame and video level features and generate predictions of videos that are similar to the other videos. We also implement several ensemble strategies to explore complementarity between both the types of provided features. The obtained results are encouraging and will impel the boundaries of research for multimedia based video recommendation systems.

cs.IR↗

arXiv2018

I Spy with My Little Eye: Analysis and Detection of Spying Browser Extensions

Several studies have been conducted on understanding third-party user tracking on the web. However, web trackers can only track users on sites where they are embedded by the publisher, thus obtaining a fragmented view of a user's online footprint. In this work, we investigate a different form of user tracking, where browser extensions are repurposed to capture the complete online activities of a user and communicate the collected sensitive information to a third-party domain. We conduct an empirical study of spying browser extensions on the Chrome Web Store. First, we present an in-depth analysis of the spying behavior of these extensions. We observe that these extensions steal a variety of sensitive user information, such as the complete browsing history (e.g., the sequence of web traversals), online social network (OSN) access tokens, IP address, and user geolocation. Second, we investigate the potential for automatically detecting spying extensions by applying machine learning schemes. We show that using a Recurrent Neural Network (RNN), the sequences of browser API calls can be a robust feature, outperforming hand-crafted features (used in prior work on malicious extensions) to detect spying extensions. Our RNN based detection scheme achieves a high precision (90.02%) and recall (93.31%) in detecting spying extensions.

cs.CR↗

arXiv2018

Under the Shadow of Sunshine: Characterizing Spam Campaigns Abusing Phone Numbers Across Online Social Networks

Cybercriminals abuse Online Social Networks (OSNs) to lure victims into a variety of spam. Among different spam types, a less explored area is OSN abuse that leverages the telephony channel to defraud users. Phone numbers are advertized via OSNs, and users are tricked into calling these numbers. To expand the reach of such scam / spam campaigns, phone numbers are advertised across multiple platforms like Facebook, Twitter, GooglePlus, Flickr, and YouTube. In this paper, we present the first data-driven characterization of cross-platform campaigns that use multiple OSN platforms to reach their victims and use phone numbers for monetization. We collect 23M posts containing 1.8M unique phone numbers from Twitter, Facebook, GooglePlus, Youtube, and Flickr over a period of six months. Clustering these posts helps us identify 202 campaigns operating across the globe with Indonesia, United States, India, and United Arab Emirates being the most prominent originators. We find that even though Indonesian campaigns generate highest volume (3.2M posts), only 1.6% of the accounts propagating Indonesian campaigns have been suspended so far. By examining campaigns running across multiple OSNs, we discover that Twitter detects and suspends 93% more accounts than Facebook. Therefore, sharing intelligence about abuse-related user accounts across OSNs can aid in spam detection. According to our dataset, around 35K victims and 8.8M USD could have been saved if intelligence was shared across the OSNs. By analyzing phone number based spam campaigns running on OSNs, we highlight the unexplored variety of phone-based attacks surfacing on OSNs.

cs.CY↗

arXiv2018

Collective Classification of Spam Campaigners on Twitter: A Hierarchical Meta-Path Based Approach

Cybercriminals have leveraged the popularity of a large user base available on Online Social Networks to spread spam campaigns by propagating phishing URLs, attaching malicious contents, etc. However, another kind of spam attacks using phone numbers has recently become prevalent on OSNs, where spammers advertise phone numbers to attract users' attention and convince them to make a call to these phone numbers. The dynamics of phone number based spam is different from URL-based spam due to an inherent trust associated with a phone number. While previous work has proposed strategies to mitigate URL-based spam attacks, phone number based spam attacks have received less attention. In this paper, we aim to detect spammers that use phone numbers to promote campaigns on Twitter. To this end, we collected information about 3,370 campaigns spread by 670,251 users. We model the Twitter dataset as a heterogeneous network by leveraging various interconnections between different types of nodes present in the dataset. In particular, we make the following contributions: (i) We propose a simple yet effective metric, called Hierarchical Meta-Path Score (HMPS) to measure the proximity of an unknown user to the other known pool of spammers. (ii) We design a feedback-based active learning strategy and show that it significantly outperforms three state-of-the-art baselines for the task of spam detection. Our method achieves 6.9% and 67.3% higher F1-score and AUC, respectively compared to the best baseline method. (iii) To overcome the problem of less training instances for supervised learning, we show that our proposed feedback strategy achieves 25.6% and 46% higher F1-score and AUC respectively than other oversampling strategies. Finally, we perform a case study to show how our method is capable of detecting those users as spammers who have not been suspended by Twitter (and other baselines) yet.

cs.SI↗

arXiv2018

The Follower Count Fallacy: Detecting Twitter Users with Manipulated Follower Count

Online Social Networks (OSN) are increasingly being used as platform for an effective communication, to engage with other users, and to create a social worth via number of likes, followers and shares. Such metrics and crowd-sourced ratings give the OSN user a sense of social reputation which she tries to maintain and boost to be more influential. Users artificially bolster their social reputation via black-market web services. In this work, we identify users which manipulate their projected follower count using an unsupervised local neighborhood detection method. We identify a neighborhood of the user based on a robust set of features which reflect user similarity in terms of the expected follower count. We show that follower count estimation using our method has 84.2% accuracy with a low error rate. In addition, we estimate the follower count of the user under suspicion by finding its neighborhood drawn from a large random sample of Twitter. We show that our method is highly tolerant to synthetic manipulation of followers. Using the deviation of predicted follower count from the displayed count, we are also able to detect customers with a high precision of 98.62%

cs.SI↗

arXiv2018

White or Blue, the Whale gets its Vengeance: A Social Media Analysis of the Blue Whale Challenge

The Blue Whale Challenge is a series of self-harm causing tasks that are propagated via online social media under the disguise of a "game." The list of tasks must be completed in a duration of 50 days and they cause both physical and mental harm to the player. The final task is to commit suicide. The game is supposed to be administered by people called "curators" who incite others to cause self-mutilation and commit suicide. The curators and potential players are known to contact each other on social networking websites and the conversations between them are suspected to take place mainly via direct messages which are difficult to track. Though, in order to find curators, the players make public posts containing certain hashtags/keywords to catch their attention. Even though a lot of these social networks have moderated posts talking about the game, yet some posts manage to pass their filters. Our research focuses on (1) understanding the social media spread of the challenge, (2) spotting the behaviour of the people taking interest in Blue Whale challenge and, (3) analysing demographics of the users who may be involved in playing the game.

cs.SI↗

arXiv2016

Me, Myself and My Killfie: Characterizing and Preventing Selfie Deaths

Over the past couple of years, clicking and posting selfies has become a popular trend. However, since March 2014, 127 people have died and many have been injured while trying to click a selfie. Researchers have studied selfies for understanding the psychology of the authors, and understanding its effect on social media platforms. In this work, we perform a comprehensive analysis of the selfie-related casualties and infer various reasons behind these deaths. We use inferences from incidents and from our understanding of the features, we create a system to make people more aware of the dangerous situations in which these selfies are taken. We use a combination of text-based, image-based and location-based features to classify a particular selfie as dangerous or not. Our method ran on 3,155 annotated selfies collected on Twitter gave 73% accuracy. Individually the image-based features were the most informative for the prediction task. The combination of image-based and location-based features resulted in the best accuracy. We have made our code and dataset available at http://labs.precog.iiitd.edu.in/killfie.

cs.SI↗

arXiv2016

Visual Themes and Sentiment on Social Networks To Aid First Responders During Crisis Events

Online Social Networks explode with activity whenever a crisis event takes place. Most content generated as part of this activity is a mixture of text and images, and is particularly useful for first responders to identify popular topics of interest and gauge the pulse and sentiment of citizens. While multiple researchers have used text to identify, analyze and measure themes and public sentiment during such events, little work has explored visual themes floating on networks in the form of images, and the sentiment inspired by them. Given the potential of visual content for influencing users' thoughts and emotions, we perform a large scale analysis to compare popular themes and sentiment across images and textual content posted on Facebook during the terror attacks that took place in Paris in 2015. Using state-of-the-art image summarization techniques, we discovered multiple visual themes which were popular in images, but were not identifiable through text. We uncovered instances of misinformation and false flag (conspiracy) theories among popular image themes, which were not prominent in user generated textual content, and can be of particular inter- est to first responders. Our analysis also revealed that while textual content posted after the attacks reflected negative sentiment, images inspired positive sentiment. To the best of our knowledge, this is the first large scale study of images posted on social networks during a crisis event.

cs.SI↗

arXiv2016

PicHunt: Social Media Image Retrieval for Improved Law Enforcement

First responders are increasingly using social media to identify and reduce crime for well-being and safety of the society. Images shared on social media hurting religious, political, communal and other sentiments of people, often instigate violence and create law & order situations in society. This results in the need for first responders to inspect the spread of such images and users propagating them on social media. In this paper, we present a comparison between different hand-crafted features and a Convolutional Neural Network (CNN) model to retrieve similar images, which outperforms state-of-art hand-crafted features. We propose an Open-Source-Intelligent (OSINT) real-time image search system, robust to retrieve modified images that allows first responders to analyze the current spread of images, sentiments floating and details of users propagating such content. The system also aids officials to save time of manually analyzing the content by reducing the search space on an average by 67%.

cs.MM↗

arXiv2016

KidsTube: Detection, Characterization and Analysis of Child Unsafe Content & Promoters on YouTube

YouTube draws large number of users who contribute actively by uploading videos or commenting on existing videos. However, being a crowd sourced and large content pushed onto it, there is limited control over the content. This makes malicious users push content (videos and comments) which is inappropriate (unsafe), particularly when such content is placed around cartoon videos which are typically watched by kids. In this paper, we focus on presence of unsafe content for children and users who promote it. For detection of child unsafe content and its promoters, we perform two approaches, one based on supervised classification which uses an extensive set of video-level, user-level and comment-level features and another based Convolutional Neural Network using video frames. Detection accuracy of 85.7% is achieved which can be leveraged to build a system to provide a safe YouTube experience for kids. Through detailed characterization studies, we are able to successfully conclude that unsafe content promoters are less popular and engage less as compared with other users. Finally, using a network of unsafe content promoters and other users based on their engagements (likes, subscription and playlist addition) and other factors, we find that unsafe content is present very close to safe content and unsafe content promoters form very close knit communities with other users, thereby further increasing the likelihood of a child getting getting exposed to unsafe content.

cs.SI↗

arXiv2016

Emotions, Demographics and Sociability in Twitter Interactions

The social connections people form online affect the quality of information they receive and their online experience. Although a host of socioeconomic and cognitive factors were implicated in the formation of offline social ties, few of them have been empirically validated, particularly in an online setting. In this study, we analyze a large corpus of geo-referenced messages, or tweets, posted by social media users from a major US metropolitan area. We linked these tweets to US Census data through their locations. This allowed us to measure emotions expressed in the tweets posted from an area, the structure of social connections, and also use that area's socioeconomic characteristics in analysis. %We extracted the structure of online social interactions from the people mentioned in tweets from that area. We find that at an aggregate level, places where social media users engage more deeply with less diverse social contacts are those where they express more negative emotions, like sadness and anger. Demographics also has an impact: these places have residents with lower household income and education levels. Conversely, places where people engage less frequently but with diverse contacts have happier, more positive messages posted from them and also have better educated, younger, more affluent residents. Results suggest that cognitive factors and offline characteristics affect the quality of online interactions. Our work highlights the value of linking social media data to traditional data sources, such as US Census, to drive novel analysis of online behavior.

cs.CY↗

arXiv2015

Abusing Phone Numbers and Cross-Application Features for Crafting Targeted Attacks

With the convergence of Internet and telephony, new applications (e.g., WhatsApp) have emerged as an important means of communication for billions of users. These applications are becoming an attractive medium for attackers to deliver spam and carry out more targeted attacks. Since such applications rely on phone numbers, we explore the feasibility, automation, and scalability of phishing attacks that can be carried out by abusing a phone number. We demonstrate a novel system that takes a potential victim's phone number as an input, leverages information from applications like Truecaller and Facebook about the victim and his / her social network, checks the presence of phone number's owner (victim) on the attack channels (over-the-top or OTT messaging applications, voice, e-mail, or SMS), and finally targets the victim on the chosen channel. As a proof of concept, we enumerate through a random pool of 1.16 million phone numbers. By using information provided by popular applications, we show that social and spear phishing attacks can be launched against 51,409 and 180,000 users respectively. Furthermore, voice phishing or vishing attacks can be launched against 722,696 users. We also found 91,487 highly attractive targets who can be attacked by crafting whaling attacks. We show the effectiveness of one of these attacks, phishing, by conducting an online roleplay user study. We found that social (69.2%) and spear (54.3%) phishing attacks are more successful than non-targeted phishing attacks (35.5%) on OTT messaging applications. Although similar results were found for other mediums like e-mail, we demonstrate that due to the significantly increased user engagement via new communication applications and the ease with which phone numbers allow collection of information necessary for these attacks, there is a clear need for better protection of OTT messaging applications.

cs.SI↗

arXiv2015

Hiding in Plain Sight: The Anatomy of Malicious Facebook Pages

Facebook is the world's largest Online Social Network, having more than 1 billion users. Like most other social networks, Facebook is home to various categories of hostile entities who abuse the platform by posting malicious content. In this paper, we identify and characterize Facebook pages that engage in spreading URLs pointing to malicious domains. We used the Web of Trust API to determine domain reputations of URLs published by pages, and identified 627 pages publishing untrustworthy information, misleading content, adult and child unsafe content, scams, etc. which are deemed as "Page Spam" by Facebook, and do not comply with Facebook's community standards. Our findings revealed dominant presence of politically polarized entities engaging in spreading content from untrustworthy web domains. Anger and religion were the most prominent topics in the textual content published by these pages. We found that at least 8% of all malicious pages were dedicated to promote a single malicious domain. Studying the temporal posting activity of pages revealed that malicious pages were more active than benign pages. We further identified collusive behavior within a set of malicious pages spreading adult and pornographic content. We believe our findings will enable technologists to devise efficient automated solutions to identify and curb the spread of malicious content through such pages. To the best of our knowledge, this is the first attempt in literature, focused exclusively on characterizing malicious Facebook pages.

cs.SI↗