A Broad Evaluation of the Tor English Content Ecosystem

Tor is among most well-known dark net in the world. It has noble uses, including as a platform for free speech and information dissemination under the guise of true anonymity, but may be culturally better known as a conduit for criminal activity and as a platform to market illicit goods and data. Past studies on the content of Tor support this notion, but were carried out by targeting popular domains likely to contain illicit content. A survey of past studies may thus not yield a complete evaluation of the content and use of Tor. This work addresses this gap by presenting a broad evaluation of the content of the English Tor ecosystem. We perform a comprehensive crawl of the Tor dark web and, through topic and network analysis, characterize the types of information and services hosted across a broad swath of Tor domains and their hyperlink relational structure. We recover nine domain types defined by the information or service they host and, among other findings, unveil how some types of domains intentionally silo themselves from the rest of Tor. We also present measurements that (regrettably) suggest how marketplaces of illegal drugs and services do emerge as the dominant type of Tor domain. Our study is the product of crawling over 1 million pages from 20,000 Tor seed addresses, yielding a collection of over 150,000 Tor pages. We make a dataset of the intend to make the domain structure publicly available as a dataset at https://github.com/wsu-wacs/TorEnglishContent.Comment: 11 page

Circadian Rhythms of Crawling and Swimming in the Nudibranch Mollusc Melibe leonina

Daily rhythms of activity driven by circadian clocks are expressed by many organisms, including molluscs. We initiated this study, with the nudibranch Melibe leonina, with four goals in mind: (1) determine which behaviors are expressed with a daily rhythm; (2) investigate which of these rhythmic behaviors are controlled by a circadian clock; (3) determine if a circadian clock is associated with the eyes or optic ganglia of Melibe, as it is in several other gastropods; and (4) test the hypothesis that Melibe can use extraocular photoreceptors to synchronize its daily rhythms to natural light-dark cycles. To address these goals, we analyzed the behavior of 55 animals exposed to either artificial or natural light-dark cycles, followed by constant darkness. We also repeated this experiment using 10 animals that had their eyes removed. Individuals did not express daily rhythms of feeding, but they swam and crawled more at night. This pattern of locomotion persisted in constant darkness, indicating the presence of a circadian clock. Eyeless animals also expressed a daily rhythm of locomotion, with more locomotion at night. The fact that eyeless animals synchronized their locomotion to the light-dark cycle suggests that they can detect light using extraocular photoreceptors. However, in constant darkness, these rhythms deteriorated, suggesting that the clock neurons that influence locomotion may be located in, or near, the eyes. Thus, locomotion in Melibe appears to be influenced by both ocular and extraocular photoreceptors, although the former appear to have a greater influence on the expression of circadian rhythms

Bots, Seeds and People: Web Archives as Infrastructure

The field of web archiving provides a unique mix of human and automated agents collaborating to achieve the preservation of the web. Centuries old theories of archival appraisal are being transplanted into the sociotechnical environment of the World Wide Web with varying degrees of success. The work of the archivist and bots in contact with the material of the web present a distinctive and understudied CSCW shaped problem. To investigate this space we conducted semi-structured interviews with archivists and technologists who were directly involved in the selection of content from the web for archives. These semi-structured interviews identified thematic areas that inform the appraisal process in web archives, some of which are encoded in heuristics and algorithms. Making the infrastructure of web archives legible to the archivist, the automated agents and the future researcher is presented as a challenge to the CSCW and archival community

Locating People of Interest in Social Networks

By representing relationships between social entities as a network, researchers can analyze them using a variety of powerful techniques. One key problem in social network analysis literature is identifying certain individuals (key players, most influential nodes) in a network. We consider the same problem in this dissertation, with the constraint that the individuals we are interested in identifying (People of Interest) are not necessarily the most important nodes in terms of the network structure. We propose an algorithm to find POIs, algorithms to collect data to find POIs, a framework to model POI behavior and an algorithm to predict POIs with guaranteed error rates. First, we propose a multi-objective optimization algorithm to find individuals who are expected to become stars in the future (rising stars), considering dynamic network data and multiple data types. Our algorithm outperforms the state of the art algorithm to find rising stars in academic data. Second, we propose two algorithms to collect data in a network crawling setting to locate POIs in dark networks. We consider potential errors that adversarial POIs can introduce to data collection process to hinder the analysis. We test and present our results on several real-world networks, and show that the proposed algorithms achieve up to a 340% improvement over the next best strategy. Next,We introduce the Adversarial Social Network Analysis game framework to model adversarial behavior of POIs towards a data collector in social networks. We run behavior experiments in Amazon Mechanical Turk and demonstrate the validity of the framework to study adversarial behavior by showing, 1) Participants understand their role, 2) Participants understand their objective in a game and, 3) Participants act as members of the adversarial group. Last, we show that node classification algorithms can be used to predict POIs in social networks. We then demonstrate how to utilize conformal prediction framework [103] to obtain guaranteed error bounds in POI prediction. Experimental results show that the Conformal Prediction framework can provide up to a 30% improvement in node classification algorithm accuracy while maintaining guaranteed error bounds on predictions