A Model of Consistent Node Types in Signed Directed Social Networks

Signed directed social networks, in which the relationships between users can be either positive (indicating relations such as trust) or negative (indicating relations such as distrust), are increasingly common. Thus the interplay between positive and negative relationships in such networks has become an important research topic. Most recent investigations focus upon edge sign inference using structural balance theory or social status theory. Neither of these two theories, however, can explain an observed edge sign well when the two nodes connected by this edge do not share a common neighbor (e.g., common friend). In this paper we develop a novel approach to handle this situation by applying a new model for node types. Initially, we analyze the local node structure in a fully observed signed directed network, inferring underlying node types. The sign of an edge between two nodes must be consistent with their types; this explains edge signs well even when there are no common neighbors. We show, moreover, that our approach can be extended to incorporate directed triads, when they exist, just as in models based upon structural balance or social status theory. We compute Bayesian node types within empirical studies based upon partially observed Wikipedia, Slashdot, and Epinions networks in which the largest network (Epinions) has 119K nodes and 841K edges. Our approach yields better performance than state-of-the-art approaches for these three signed directed networks.Comment: To appear in the IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), 201

Context Map Analysis of Fake News in Social Media: A Contextualized Visualization Approach

Visualization tools in text analytics are typically based on content analysis, using $n$-gram frequencies or topic models which output commonly used words, phrases, or topics in a text corpus. However, the interpretation of these visual output and summary labels can be incomplete or misleading when words or phrases are taken out of context. We use a novel Context Map approach to create a connected network of $n$-grams by considering the frequency in which they are used together in the same context. We combine network optimization techniques with embedded representation models to generate an visualization interface with clearer and more accurate interpretation potential. In this paper, we apply our Context Map method to analyze fake news in social media. We compare news article veracity (true versus false news) with orientation (left, mainstream, or right). Our approach provides a rich context analysis of the language used in true versus fake news

Social Overhead Capital Development and Geographical Concentration

In recent economic geography, it is emphasized that the effect of cost decreasing in transportation on agglomeration is nonlinear. It is said that the influence of traffic infrastructure investment and the change in transportation cost on urban agglomeration does not appear until the cost is below a certain amount, and that once agglomeration arises that effect would be kept with higher probability. In theoretical models such as Krugman (1991) and Fujita, Krugman and Venables (1999), multiple equilibria and path dependence are emphasized, as well as non linearity. Those models are intuitive, but it is hard to have a statistical analysis because of the non linearity. About the macroeconomic effect of social overhead capital investment, starting from the analysis by Aschauer (1985, 1989), a lot of empirical research has been done on the productivity effect of social capital. For example, we have Asako et al. (1994), Mitsui and Ohta (1995). Moreover, Roback (1982) uses the Hedonic approach to find the effect of amenity-based social overhead capital (related to waste disposal plants, or sewage facilities), followed by Mitsui and Hayashi (2001) for a Japanese case. In these Japanese studies, they are only concerned about the topic about inefficiency of the social overhead capital distribution but not about theoretical progress in urban economics. If Krugmans model is true, however, there is a possibility that rural traffic infrastructure investment for the purpose of redistribution will experience both a decline in rural areas and agglomeration into urban areas. In the following, we will examine general theory about how we should observe the effect of traffic network provision in section II. We will estimate a market potential function and an index with which the geographical concentration degree is measured, and see how the agglomeration degree has changed historically. In section II we will conduct analysis through using prefecture data and municipal data, particularly in the Kyushu district 2 .capital development, potential function, geographical concentration degree, Kyushu district, Japan, Public Policy, network effect

Exploring Cyberterrorism, Topic Models and Social Networks of Jihadists Dark Web Forums: A Computational Social Science Approach

This three-article dissertation focuses on cyber-related topics on terrorist groups, specifically Jihadists’ use of technology, the application of natural language processing, and social networks in analyzing text data derived from terrorists\u27 Dark Web forums. The first article explores cybercrime and cyberterrorism. As technology progresses, it facilitates new forms of behavior, including tech-related crimes known as cybercrime and cyberterrorism. In this article, I provide an analysis of the problems of cybercrime and cyberterrorism within the field of criminology by reviewing existing literature focusing on (a) the issues in defining terrorism, cybercrime, and cyberterrorism, (b) ways that cybercriminals commit a crime in cyberspace, and (c) ways that cyberterrorists attack critical infrastructure, including computer systems, data, websites, and servers. The second article is a methodological study examining the application of natural language processing computational techniques, specifically latent Dirichlet allocation (LDA) topic models and topic network analysis of text data. I demonstrate the potential of topic models by inductively analyzing large-scale textual data of Jihadist groups and supporters from three Dark Web forums to uncover underlying topics. The Dark Web forums are dedicated to Islam and the Islamic world discussions. Some members of these forums sympathize with and support terrorist organizations. Results indicate that topic modeling can be applied to analyze text data automatically; the most prevalent topic in all forums was religion. Forum members also discussed terrorism and terrorist attacks, supporting the Mujahideen fighters. A few of the discussions were related to relationships and marriages, advice, seeking help, health, food, selling electronics, and identity cards. LDA topic modeling is significant for finding topics from larger corpora such as the Dark Web forums. Implications for counterterrorism include the use of topic modeling in real-time classification and removal of online terrorist content and the monitoring of religious forums, as terrorist groups use religion to justify their goals and recruit in such forums for supporters. The third article builds on the second article, exploring the network structures of terrorist groups on the Dark Web forums. The two Dark Web forums\u27 interaction networks were created, and network properties were measured using social network analysis. A member is considered connected and interacting with other forum members when they post in the same threads forming an interaction network. Results reveal that the network structure is decentralized, sparse, and divided based on topics (religion, terrorism, current events, and relationships) and the members\u27 interests in participating in the threads. As participation in forums is an active process, users tend to select platforms most compatible with their views, forming a subgroup or community. However, some members are essential and influential in the information and resources flow within the networks. The key members frequently posted about religion, terrorism, and relationships in multiple threads. Identifying key members is significant for counterterrorism, as mapping network structures and key users are essential for removing and destabilizing terrorist networks. Taken together, this dissertation applies a computational social science approach to the analysis of cyberterrorism and the use of Dark Web forums by jihadists

AN ANALYSIS OF COVID-19 MISINFORMATION ON THE TELEGRAM SOCIAL NETWORK

The proliferation of misinformation groups and users on social networks has illustrated the need for targeted misinformation detection, analysis, and countering techniques. For example, in 2018, Twitter disclosed research that identified more than 50,000 malicious accounts linked to foreign-backed agencies that used the social network to spread propaganda and influence voters during the 2016 U.S. presidential election. Twitter also began removing and labeling content as misinformation during the 2020 U.S. election, which led to an influx of users to social networks, such as Telegram. Telegram’s dedication to free speech and privacy is an attractive platform for misinformation groups and thus provides a unique opportunity to observe and measure how unabated ideas and sentiments evolve and spread. In this thesis, we create a dataset by crawling channels and groups in Telegram that are centered around COVID-19 and vaccine conversations. For analysis, we first analyze the topics and sentiments of the data using machine learning models. Next, we analyze the time series relationship between sentiment and topic trends. Then, we look for topic relationships by clustering performed on topic-based graph networks. Lastly, we cluster channels using document vectors to identify super-groups of related conversations. We conclude that Telegram communities risk producing echo chamber effects and are potential targets for external actors to embed and grow misinformation without hindrance.Lieutenant, United States NavyApproved for public release. Distribution is unlimited