Clickbait Detection Using Deep Recurrent Neural Network

People who use social networks often fall prey to clickbait, which is commonly exploited by scammers. The scammer attempts to create a striking headline that attracts the majority of users to click an attached link. Users who follow the link can be redirected to a fraudulent resource, where their personal data are easily extracted. To solve this problem, a novel browser extension named ClickBaitSecurity is proposed, which helps to evaluate the security of a link. The novel extension is based on the legitimate and illegitimate list search (LILS) algorithm and the domain rating check (DRC) algorithm. Both of these algorithms incorporate binary search features to detect malicious content more quickly and more efficiently. Furthermore, ClickBaitSecurity leverages the features of a deep recurrent neural network (RNN). The proposed ClickBaitSecurity solution has greater accuracy in detecting malicious and safe links compared to existing solutions.

Smart Tab Predictor: A Chrome Extension to Assist Browser Task Management using Machine Learning and Data Analysis

The outbreak of the Covid 19 pandemic has forced most schools and businesses to use digital learning and working. Many people have repetitive web browsing activities or encounter too many open tabs causing slowness in surfing the websites. This paper presents a tab predictor application, a Chrome browser extension that uses Machine Learning (ML) to predict the next URL to open based on the time and frequency of current and previous tabs. Nowadays, AI technology has expanded in people’s daily lives like self-driving cars and assistive-type robots. The AI ML module in our application is more basic and is built using Python and Scikit-Learn (Sklearn) machine learning libraries. We use JavaScript and Chrome API to collect the browser tab data and store it in a Firebase Cloud Firestore. The ML module then loads data from the Firebase, trains datasets to adapt to a user’s patterns, and predicts URLs to recommend opening new URLs. For Machine Learning, we compare three ML models and select the Random Forest Classifier. We also apply SMOTE (Synthetic Minority Oversampling Technique) to make the data-set more balanced, thus improving the prediction accuracy. Both manual tests and Cross Validation are performed to verify the predicted URLs. As a result, using the Smart Tab Predictor application will help students and business workers manage the web browser tabs more efficiently in their daily routine for online classes, online meetings, and other websites.

YourAdvalue: Measuring Advertising Price Dynamics without Bankrupting User Privacy

The Real Time Bidding (RTB) protocol is by now more than a decade old. During this time, a handful of measurement papers have looked at bidding strategies, personal information flow, and cost of display advertising through RTB. In this paper, we present YourAdvalue, a privacy-preserving tool for displaying to end-users in a simple and intuitive manner their advertising value as seen through RTB. Using YourAdvalue, we measure desktop RTB prices in the wild, and compare them with desktop and mobile RTB prices reported by past work. We present how it estimates ad prices that are encrypted, and how it preserves user privacy while reporting results back to a data-server for analysis. We deployed our system, disseminated its browser extension, and collected data from 200 users, including 12000 ad impressions over 11 months. By analyzing this dataset, we show that desktop RTB prices have grown 4.6x over desktop RTB prices measured in 2013, and 3.8x over mobile RTB prices measured in 2015. We also study how user demographics associate with the intensity of RTB ecosystem tracking, leading to higher ad prices. We find that exchanging data between advertisers and/or data brokers through cookie-synchronization increases the median value of display ads by 19%. We also find that female and younger users are more targeted, suffering more tracking (via cookie synchronization) than male or elder users. As a result of this targeting in our dataset, the advertising value (i) of women is 2.4x higher than that of men, (ii) of 25-34 year-olds is 2.5x higher than that of 35-44 year-olds, (iii) is most expensive on weekends and early mornings.

Tumblr’s Xkit Guy, social media modding, and code as resistance

The platformalization of the internet means that fan communities must make homes in spaces that they do not own. Tumblr has lately been the chosen home for many online fandoms because of its affordances for anonymity and lack of censorship. However, the profit motives of Tumblr's owners, especially after Yahoo purchased the site in 2013, are frequently at odds with the affordances that nourish fan communities. Fans on Tumblr are aware of their precarious position, where a few keystrokes by a developer could endanger an affordance that their communities depend on. An examination of the relationship between Tumblr users and Tumblr staff provides a case study of how fan communities push back against platform owners. The Tumblr Xkit Extension, a fan-made browser extension maintained by the volunteer labor of the Xkit Guy, is used to illustrate that the Tumblr community acts as a fandom of a social media site. This lets us understand the Xkit Browser extension as a resistant fan work written in the medium of code. Like video game modding, social media modding is a transformative work that permits fans to oppose the platform's code as law—but one that could also constitute a form of exploited labor.

Flow of online misinformation during the peak of the COVID-19 pandemic in Italy

The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network.We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community.

Dynamic Identification of Learning Styles in MOOC Environment Using Ontology Based Browser Extension

With the advent of the era of big data and Web 3.0 on the horizon, different types of online deliverable resources in the pedagogical field have also become raft. Massive Open Online Courses (MOOCs) are the most important of such learning resources that provide many courses at different levels for the learners on the go. The data generated by these MOOCs, however, is often unorganized and difficult to track or is not used to the extent that allows identification of learner types to facilitate better learning. The proposed approach in this paper aims to detect the learning style of a learner, interacting with the MOOC portal, dynamically and automatically through a novel, indigenous and in-built browser extension. This extension is used to capture the usage parameters of the learner and analyze learning behavior in real-time. The usage parameters are captured and stored as a learner ontology to ease sharing and operating across different platforms. The learning style so deduced is based on the Felder Silverman Learning Style Model (FSLSM), where learner’s behavior under multiple criteria, vis-`a-vis perception, input, understanding, and processing are measured. Based on the generated ontological semantics of learner’s behavior, multiple models can be made to facilitate precise and efficient learning. The result shows that this state-of-the-art approach identifies and detects the learning styles of the learners automatically and dynamically, i.e., changing over time

An Expedient Way to Organize Passwords

There are billions of passwords in the world today, with more being created every hour of every day, making them a bane of the modern era. The usage of passwords is becoming more common despite the inadequacies [1]. This is making it more tedious and overwhelming for the users to manage, with the passwords being long and unique. In this paper, we propose a solution to securely manage and organize passwords while requiring the user to keep track of only a single password. As our solution is in the form of a browser extension, there is no need for server-side changes. Unlike other password managers, our extension is a lightweight application and is highly resistant to brute force attacks. We discuss the need for a password manager, the construction of our extension along with the security overview.