Abstract Embedding news articles is a crucial tool for various fields, including media bias detection, fake news identification, and news recommendation systems. However, existing news embedding methods are not optimized to capture the latent context of news events. Most embedding methods rely on full-text information, neglecting the generation of time-relevant embeddings. In this paper, we propose a novel, lightweight method that optimizes news embedding generation by focusing on entities and themes mentioned in articles and their historical connections to specific events. We suggest a three-stage method. First, we process and extract events, entities, and themes from news articles. Second, we generate periodic time embeddings for themes and entities by training time-separated GloVe models on current and historical data. Lastly, we concatenate the news embeddings generated by two distinct approaches: Smooth Inverse Frequency (SIF) for article-level vectors and Siamese Neural Networks for embeddings with nuanced event-related information. We leveraged over 850,000 news articles and one million events from the GDELT project to test and evaluate our method. We conducted a comparative analysis of different news embedding generation methods for validation. Our experiments demonstrate that our approach improves and outperforms state-of-the-art methods on shared event detection tasks.

The unprecedented growth in video conferencing usage is accompanied by multiple security and privacy threats. Importantly, protecting users’ privacy is not always in their own hands. Posting meeting images affects all participants, leading to an easy collection of personal data including age, gender and linkage with participation in other meetings. Here, we explored privacy issues that may be at risk by attending virtual meetings. We extracted private information from collage images of meeting participants that are publicly posted online. We used image processing, text recognition tools, as well as social network analysis to explore our curated dataset of over 15 700 collage images, and over 142 000 face images of meeting participants. We demonstrate that video conference users are facing prevalent security and privacy threats. Our results indicate that it is relatively easy to collect thousands of publicly available images of video conference meetings and extract personal information about the participants, including their face images, age, gender, usernames, and even full names. This type of data can vastly and easily jeopardize people’s security and privacy both in the online and real-world, affecting not only adults but also more vulnerable segments of society, such as children and older adults. Finally, we show that cross-referencing facial image data with social network data may put participants at additional privacy risks they may not be aware of and that it is possible to identify users that appear in several video conference meetings, thus providing a potential to maliciously aggregate different sources of information about a target individual.

Video conferencing (VC) has become increasingly popular, bringing new challenges in privacy and security, one notable example is of Zoombombing. Furthermore, other issues related to VC usage have emerged, such as keeping students involved. Identifying abnormal segments in VC meetings in vast data is a challenging task. Here, we introduce a novel algorithm to detect such anomalies in VC automatically. By analyzing publicly available VC recordings, our algorithm tracks and analyzes changes in participants’ facial expressions to identify and quantity overall meeting climate changes. We demonstrate performance of 92.3% precision in anomaly detection on the collected dataset. Our model offers a pioneering solution for recognizing abnormal events in VC meetings.

User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backward to manipulate users’ feedback or place phony bids in order to increase their own sales and harm competitors. The black market of user feedback, supported by a plethora of shill bidders, prospers on top of legitimate electronic commerce. In this paper, we investigate the ecosystem of shill bidders based on large-scale data by analyzing hundreds of millions of users who performed billions of transactions, and we propose a machine-learning-based method for identifying communities of users that methodically provide dishonest feedback. Our results show that (1) shill bidders can be identified with high precision based on their transaction and feedback statistics; and (2) in contrast to legitimate buyers and sellers, shill bidders form cliques to support each other.

Entity Matching is an essential part of all real-world systems that take in structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and integration processes require completion before any data analytics, or further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights, supports data visualization, and guides further analytic approaches to the data. Here, we focus on organization entity matching. We introduce CompanyName2Vec, a novel algorithm to solve company entity matching (CEM) using a neural network model to learn company name semantics from a job ad corpus, without relying on any information on the matched company besides its name. Based on a real-world data, we show that CompanyName2Vec outperforms other evaluated methods and solves the CEM challenge with an average success rate of 89.3%.

Online genealogy datasets contain extensive information about millions of people and their past and present family connections. This vast amount of data can help identify various patterns in the human population. In this study, we present methods and algorithms that can assist in identifying variations in lifespan distributions of the human population in the past centuries, in detecting social and genetic features that correlate with the human lifespan, and in constructing predictive models of human lifespan based on various features that can easily be extracted from genealogy datasets. We have evaluated the presented methods and algorithms on a large online genealogy dataset with over a million profiles and over 9 million connections, all of which were collected from the WikiTree website. Our findings indicate that significant but small positive correlations exist between the parents’ lifespan and their children’s lifespan. Additionally, we found slightly higher and significant correlations between the lifespans of spouses. We also discovered a very small positive and significant correlation between longevity and reproductive success in males, and a small and significant negative correlation between longevity and reproductive success in females. Moreover, our predictive models presented results with a Mean Absolute Error as low as 13.18 in predicting the lifespans of individuals who outlived the age of 10, and our classification models presented better than random classification results in predicting which people who outlive the age of 50 will also outlive the age of 80. We believe that this study will be the first of many studies to utilize the wealth of data on human populations, existing in online genealogy datasets, to better understand factors that influence the human lifespan. Understanding these factors can assist scientists in providing solutions for successful aging.

One dimension on the Internet, which has gained great popularity in recent years are the online social networks (OSNs). Users all over the globe write, share, and publish personal information about themselves, their friends, and their workplace. In this study we present a method for infiltrating specific users in targeted organizations by using organizational social networks topologies and Socialbots. The targeted organizations, which have been chosen by us, were technology-oriented organizations. Employees from this kind of organization should be more aware of the dangers of exposing private information. An infiltration is defined as accepting a Socialbot's friend request. Upon accepting a Socialbot's friend request, users unknowingly expose information about themselves and their workplace. To infiltrate this we had to use our Socialbots in a sophisticated manner. First, we had to gather information and recognize Facebook users who work in targeted organizations. Afterwards, we chose ten Facebook users from every targeted organization randomly. These ten users were chosen to be the specific users from targeted organizations of which we would like to infiltrate. The Socialbots sent friend requests to all specific users' mutual friends who worked or work in the same targeted organization. The rationale behind this idea was to gain as many mutual friends as possible and through this act increase the probability that our friend requests will be accepted by the targeted users. We tested the proposed method on targeted users from two different organizations. Our method was able to gain a successful percentage of 50% and 70% respectively. The results demonstrate how easily adversaries can infiltrate users they do not know and get full access to personal and valuable information. These results are more surprising when we emphasize the fact that we chose oriented users who should be more aware to the dangers of information leakage for this study on purpose. Moreover, the results indicate once again that users who are interested in protecting themselves should not disclose information in OSNs and should be cautious of accepting friendship requests from unknown persons.

Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds of millions of users. Unfortunately, links between individuals may be missing either due to an imperfect acquirement process or because they are not yet reflected in the online network (i.e., friends in the real world did not form a virtual connection). The primary bottleneck in link prediction techniques is extracting the structural features required for classifying links. In this article, we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links. We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a predicament of classifying links between individuals with at least one common friend. We also present a method for calculating the amount of data needed in order to build more accurate classifiers. The new Friends measure and Same community features we developed are shown to be good predictors for missing links. An evaluation experiment was performed on ten large social networks datasets: Academia.edu, DBLP, Facebook, Flickr, Flixster, Google+, Gowalla, TheMarker, Twitter, and YouTube. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in online social networks.