Link-based similarity search to fight web spam

www.ilab.sztaki.hu/websearch We investigate the usability of similarity search in fighting Web spam based on the assumption that an unknown spam page is more similar to certain known spam pages than to honest pages. In order to be successful, search engine spam never appears in isolation: we observe link farms and alliances for the sole purpose of search engine ranking manipulation. The artificial nature and strong inside connectedness however gave rise to successful algorithms to identify search engine spam. One example is trust and distrust propagation, an idea originating in recommender systems and P2P networks, that yields spam classificators by spreading information along hyperlinks from white and blacklists. While most previous results use PageRank variants for propagation, we form classifiers by investigating similarity top lists of an unknown page along various measures such as co-citation, companion, nearest neighbors in low dimensional projections and SimRank. We test our method over two data sets previously used to measure spam filtering algorithms. 1

Pattern Discrimination

Algorithmic identity politics reinstate old forms of social segregation - in a digital world, identity politics is pattern discrimination. It is by recognizing patterns in input data that Artificial Intelligence algorithms create bias and practice racial exclusions thereby inscribing power relations into media. How can we filter information out of data without reinserting racist, sexist, and classist beliefs

Water Conservation

To conserve water, one of the most valuable and vital resources in the world, management and public strategies, processes to reduce water consumption in industrial/commercial applications, and methods such as smart irrigation systems have been proposed. Local authorities have focused on infrastructure operations to prevent water losses and flow measurements have begun to be followed more closely. The use of greywater for partial recycling of water for household purposes and rainwater harvesting systems are being encouraged. In addition, there is more research on water conservation, its smart use, and recycling of used water being conducted. This book presents valuable scientific research on water and land management, groundwater management, and water/wastewater treatment applications for the conservation of water

Connecting the Dots. Intelligence and Law Enforcement since 9/11

This work examines how the conceptualization of knowledge as both problem and solution reconfigured intelligence and law enforcement after 9/11. The idea was that more information should be collected, and better analyzed. If the intelligence that resulted was shared, then terrorists could be identified, their acts predicted, and ultimately prevented. Law enforcement entered into this scenario in the United States, and internationally. “Policing terrorism” refers to the engagement of state and local law enforcement in intelligence, as well as approaching terrorism as a legal crime, in addition to or as opposed to an act of war. Two venues are explored: fusion centers in the United States and the international organization of police, Interpol. The configuration can be thought of schematically as operating through the set of law, discipline and security. Intelligence is predominantly a security approach. It modulates that within its purview, wielding the techniques and technologies that are here discussed. The dissertation is divided into two sections: Intelligence and Policing Terrorism. In the first, intelligence is taken up as a term, and its changes in referent and concept are examined. The Preface and Chapter One present a general introduction to the contemporary situation and intelligence, via Sherman Kent, as knowledge, organization and activities. Chapter Two traces the development of intelligence in the United States as a craft and profession. Chapter Three discusses some of the issues involving the intersection of intelligence and policy, and how those manifested in the aftermath of 9/11 and the lead up to the 2003 invasion of Iraq. The second section examines the turn to policing terrorism, beginning, in Chapter Four, with how Interpol has dealt with bioterrorism, and an examination of the shifting conceptualization of biological threats in international law. Moving from threats to their consequences, Chapter Five takes up the concept of an event in order to analyze the common comparison of Pearl Harbor and 9/11. Chapters Six and Seven turn to fieldwork done in the United States, with an examination of the suspicious activity reporting system and law enforcement’s inclusion in the Information Sharing Environment, focusing on fusion centers and data mining

On the Promotion of the Social Web Intelligence

Given the ever-growing information generated through various online social outlets, analytical research on social media has intensified in the past few years from all walks of life. In particular, works on social Web intelligence foster and benefit from the wisdom of the crowds and attempt to derive actionable information from such data. In the form of collective intelligence, crowds gather together and contribute to solving problems that may be difficult or impossible to solve by individuals and single computers. In addition, the consumer insight revealed from social footprints can be leveraged to build powerful business intelligence tools, enabling efficient and effective decision-making processes. This dissertation is broadly concerned with the intelligence that can emerge from the social Web platforms. In particular, the two phenomena of social privacy and online persuasion are identified as the two pillars of the social Web intelligence, studying which is essential in the promotion and advancement of both collective and business intelligence. The first part of the dissertation is focused on the phenomenon of social privacy. This work is mainly motivated by the privacy dichotomy problem. Users often face difficulties specifying privacy policies that are consistent with their actual privacy concerns and attitudes. As such, before making use of social data, it is imperative to employ multiple safeguards beyond the current privacy settings of users. As a possible solution, we utilize user social footprints to detect their privacy preferences automatically. An unsupervised collaborative filtering approach is proposed to characterize the attributes of publicly available accounts that are intended to be private. Unlike the majority of earlier studies, a variety of social data types is taken into account, including the social context, the published content, as well as the profile attributes of users. Our approach can provide support in making an informed decision whether to exploit one\u27s publicly available data to draw intelligence. With the aim of gaining insight into the strategies behind online persuasion, the second part of the dissertation studies written comments in online deliberations. Specifically, we explore different dimensions of the language, the temporal aspects of the communication, as well as the attributes of the participating users to understand what makes people change their beliefs. In addition, we investigate the factors that are perceived to be the reasons behind persuasion by the users. We link our findings to traditional persuasion research, hoping to uncover when and how they apply to online persuasion. A set of rhetorical relations is known to be of importance in persuasive discourse. We further study the automatic identification and disambiguation of such rhetorical relations, aiming to take a step closer towards automatic analysis of online persuasion. Finally, a small proof of concept tool is presented, showing the value of our persuasion and rhetoric studies

Information Diffusion and Summarization in Social Networks

Social networks are web-based services that allow users to connect and share information. Due to the huge size of social network graph and the plethora of generated content, it is difficult to diffuse and summarize the social media content. This thesis thus addresses the problems of information diffusion and information summarization in social networks. Information diffusion is a process by which information about new opinions, behaviors, conventions, practices, and technologies flow from person-to-person through a social network. Studies on information diffusion primarily focus on how information diffuses in networks and how to enhance information diffusion. Our aim is to enhance the information diffusion in social networks. Many factors affect information diffusion, such as network connectivity, location, posting timestamp, post content, etc. In this thesis, we analyze the effect of three of the most important factors of information diffusion, namely network connectivity, posting time and post content. We first study the network factor to enhance the information diffusion, and later analyze how time and content factors can diffuse the information to a large number of users. Network connectivity of a user determines his ability to disseminate information. A well-connected authoritative user can disseminate information to a more wider audience compared to an ordinary user. We present a novel algorithm to find topicsensitive authorities in social networks. We use the topic-specific authoritative position of the users to promote a given topic through word-of-mouth (WoM) marketing. Next, the lifetime of social media content is very short, which is typically a few hours. If post content is posted at the time when the targeted audience are not online or are not interested in interacting with the content, the content will not receive high audience reaction. We look at the problem of finding the best posting time(s) to get high information diffusion. Further, the type of social media content determines the amount of audience interaction, it gets in social media. Users react differently to different types of content. If a post is related to a topic that is more arousing or debatable, then it tends to get more comments. We propose a novel method to identify whether a post has high arousal content or not. Furthermore, the sentiment of post content is also an important factor to garner users’ attention in social media. Same information conveyed with different sentiments receives a different amount of audience reactions. We understand to what extent the sentiment policies employed in social media have been successful to catch users’ attention. Finally, we study the problem of information summarization in social networks. Social media services generate a huge volume of data every day, which is difficult to search or comprehend. Information summarization is a process of creating a concise readable summary of this huge volume of unstructured information. We present a novel method to summarize unstructured social media text by generating topics similar to manually created topics. We also show a comprehensive topical summary by grouping semantically related topics