Supraja Gurajala

Bio: Supraja Gurajala is an academic researcher from State University of New York at Potsdam. The author has contributed to research in topics: Air quality index & Traffic intensity. The author has an hindex of 4, co-authored 8 publications receiving 117 citations. Previous affiliations of Supraja Gurajala include Clarkson University.

Fake Twitter accounts: profile characteristics obtained using an activity-based pattern detection approach

Abstract: In Online Social Networks (OSNs), the audience size commanded by an organization or an individual is a critical measure of that entity's popularity. This measure has important economic and/or political implications. Organizations can use information about their audience, such as age, location etc., to tailor their products or their message appropriately. But such tailoring can be biased by the presence of fake profiles on these networks. In this study, analysis of 62 million publicly available Twitter user profiles was conducted and a strategy to retroactively identify automatically generated fake profiles was established. Using a pattern-matching algorithm on screen-names with an analysis of tweet update times, a highly reliable sub-set of fake user accounts were identified. Analysis of profile creation times and URLs of these fake accounts revealed distinct behavior of the fake users relative to a ground truth data set. The combination of this scheme with established social graph analysis will allow for time-efficient detection of fake profiles in OSNs.

Profile characteristics of fake Twitter accounts

Abstract: In online social networks, the audience size commanded by an organization or an individual is a critical measure of that entity’s popularity and this measure has important economic and/or political...

Understanding Public Response to Air Quality Using Tweet Analysis

Abstract: Poor air quality is recognized as a major risk factor for human health globally. Critical to addressing this important public-health issue is the effective dissemination of air quality data, inform...

A MAC protocol to support QoS for multimedia traffic transmission over ad hoc networks

Abstract: Multimedia communication over wireless networks has become the driving technology for many important applications, experiencing dramatic market growth and promising revolutionary experiences in personal communication, gaming, entertainment, military, security, environment monitoring, and more. The advances in wireless communications and growth of real-time applications have necessitated the development of wireless networks that can support high Quality of Service (QoS). In this paper, we introduce a reservation based, asynchronous MAC protocol called Multi-rate Multi-hop MAC Protocol (MMMP) for multi-hop Ad hoc networks that provides QoS guarantees for multimedia traffic. MMMP achieves this by providing service differentiation for multi-rate real-time traffic (both real time constant bit rate traffic and real time variable bit rate traffic) and guaranteeing a bounded end-to-end delay for the same while still catering to the throughput requirements of non real time traffic. In addition, it administers bandwidth preservation via a feature called 'Smart Drop' and implements efficient bandwidth usage through a mechanism called 'Release Bandwidth'. Simulation results on the QualNet simulator indicate that MMMP outperforms IEEE 802.11 on all performance metrics and can efficiently handle a large range of traffic intensity. It also outperforms other similar state of the art MAC protocols.

The Environmental Protection Agency

Abstract: The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter

Abstract: In late March of 2020, a new hashtag, #FilmYourHospital, made its first appearance on social media. The hashtag encouraged people to visit local hospitals to take pictures and videos of empty hospi...

A New Application of Social Impact in Social Media for Overcoming Fake News in Health.

Abstract: One of the challenges today is to face fake news (false information) in health due to its potential impact on people's lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media.

Using Machine Learning to Detect Fake Identities: Bots vs Humans

Abstract: There are a growing number of people who hold accounts on social media platforms (SMPs) but hide their identity for malicious purposes Unfortunately, very little research has been done to date to detect fake identities created by humans, especially so on SMPs In contrast, many examples exist of cases where fake accounts created by bots or computers have been detected successfully using machine learning models In the case of bots these machine learning models were dependent on employing engineered features, such as the “friend-to-followers ratio” These features were engineered from attributes, such as “friend-count” and “follower-count,” which are directly available in the account profiles on SMPs The research discussed in this paper applies these same engineered features to a set of fake human accounts in the hope of advancing the successful detection of fake identities created by humans on SMPs