11,342 research outputs found

    Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

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    Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

    Untangling Neoliberalism’s Gordian Knot: Cancer Prevention and Control Services for Rural Appalachian Populations

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    In eastern Kentucky, as in much of central Appalachia, current local storylines narrate the frictions and contradictions involved in the structural transition from a post-WWII Fordist industrial economy and a Keynesian welfare state to a Post-Fordist service economy and Neoliberal hollow state, starving for energy to sustain consumer indulgence (Jessop, 1993; Harvey, 2003; 2005). Neoliberalism is the ideological force redefining the “societal infrastructure of language” that legitimates this transition, in part by redefining the key terms of democracy and citizenship, as well as valorizing the market, the individual, and technocratic innovation (Chouliaraki & Fairclough, 1999; Harvey, 2005). This project develops a perspective that understands cancer prevention and control in Appalachiaas part of the structural transition that is realigning community social ties in relation to ideological forces deployed as “commonsense” storylines that “lubricate” frictions that complicates the transition

    Computing tie strength

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    Relationships make social media social. But, not all relationships are created equal. We have colleagues with whom we correspond intensely, but not deeply; we have childhood friends we consider close, even if we fell out of touch. Social media, however, treats everybody the same: someone is either a completely trusted friend or a total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the name tie strength, a term for the strength of a relationship between two people. Despite many compelling findings along this line of research, social media does not incorporate tie strength or its lessons. Neither does most research on large-scale social phenomena. In social network analyses, a link either exists or not. Relationships have few properties of their own. Simply put, we do not understand a basic property of relationships expressed online. This dissertation addresses this problem, merging the theories behind tie strength with the data from social media. I show how to reconstruct tie strength from digital traces in online social media, and how to apply it as a tool in design and analysis. Specifically, this dissertation makes three contributions. First, it offers a rich, high-accuracy and general way to reconstruct tie strength from digital traces, traces like recency and a message???s emotional content. For example, the model can split users into strong and weak ties with nearly 89% accuracy. I argue that it also offers us a chance to rethink many of social media???s most fundamental design elements. Next, I showcase an example of how we can redesign social media using tie strength: a Twitter application open to anyone on the internet which puts tie strength at the heart of its design. Through this application, called We Meddle, I show that the tie strength model generalizes to a new online community, and that it can solve real people???s practical problems with social media. Finally, I demonstrate that modeling tie strength is an important new tool for analyzing large-scale social phenomena. Specifically, I show that real-life diffusion in online networks depends on tie strength (i.e., it depends on social relationships). As a body of work, diffusion studies make a big simplifying assumption: simple stochastic rules govern person-to-person transmission. How does a disease spread? With constant probability. How does a chain letter diffuse? As a branching process. I present a case where this simplifying assumption does not hold. The results challenge the macroscopic diffusion properties in today???s literature, and they hint at a nest of complexity below a placid stochastic surface. It may be fair to see this dissertation as linking the online to the offline; that is, it connects the traces we leave in social media to how we feel about relationships in real life

    Identifying Cohesive Local Community Structures in Networks

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    Identifying community structure in networks is an important topic in data mining research. One of the challenges is to find local communities without requiring the global knowledge of the entire network. Exiting techniques have several limitations. First, there is no widely accepted definition for community. Second, these algorithms either lack good stopping criteria or depend on predefined threshold parameters. In this research I propose a local cohesion based algorithm to identify local communities in networks. This algorithm is grounded on the widely accepted group cohesion definition in social network analysis research. The algorithm is self-contained and does not depend on predefined threshold parameter to terminate the identification process. The evaluation results show that the proposed algorithm is more effective than the benchmark algorithm and can identify meaningful local communities in very large networks such as the Amazon co-purchasing network

    SAMPLING AND CHARACTERIZING EVOLVING COMMUNITIES IN SOCIAL NETWORKS

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    One of the most important structures in social networks is communities. Understanding communities is useful in many applications, such as suggesting a friend for a user in an online friendship network, recommending a product for a user in an e-commerce network, etc. However, before studying anything about communities, researchers first need to collect appropriate data. Getting complete access to the data for community studies is unrealistic in most cases. In this work, we address the problem of crawling networks to identify community structure. Firstly, we present a network sampling technique to crawl the community structure of dynamic networks when there is a limitation on the number of nodes that can be queried. The process begins by obtaining a sample for the first-time step. In subsequent time steps, the crawling process is guided by community structure discoveries made in the past. Experiments conducted on the proposed approach and certain baseline techniques reveal the proposed approach has at least a 35% performance increase in cases when the total query budget is fixed over the entire period and at least an 8% increase in cases when the query budget is fixed per time step. Secondly, we propose a sampling technique to sample communities in node attributed edge streams when there is a limit on the maximum number of nodes that can be stored. The process learns if the nodal information can characterize communities. The nodal information is leveraged with the structural information to generate representative communities. If the nodal information does not characterize communities, only structural information is considered in assigning nodes to communities. The proposed approach provides a performance improvement of up to about 5 times that of baselines. Finally, we investigate factors that characterize the evolution of communities with respect to the number of active users. We perform this investigation on the Reddit social media platform. We begin by first analyzing individual conversations of one community and sees how that generalizes to other communities. The first community studied is Reddit’s changemyview. The changemyview community, in addition to its rich data source, has an interesting property where members whose view are changed award points to users that successfully changed their minds. From the changemyview community, we observe that the linguistic style and interactions of members of the community can significantly differentiate susceptible and non-susceptible users. Next, we examine other communities (subreddits), and investigate how the user behaviors observed from changemyview relate to patterns of community evolution. We learn that the linguistic style and interactions of members in a community can also significantly differentiate the different parts of the evolution of the community with respect to number of active users

    Mitigating Colluding Attacks in Online Social Networks and Crowdsourcing Platforms

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    Online Social Networks (OSNs) have created new ways for people to communicate, and for companies to engage their customers -- with these new avenues for communication come new vulnerabilities that can be exploited by attackers. This dissertation aims to investigate two attack models: Identity Clone Attacks (ICA) and Reconnaissance Attacks (RA). During an ICA, attackers impersonate users in a network and attempt to infiltrate social circles and extract confidential information. In an RA, attackers gather information on a target\u27s resources, employees, and relationships with other entities over public venues such as OSNs and company websites. This was made easier for the RA to be efficient because well-known social networks, such as Facebook, have a policy to force people to use their real identities for their accounts. The goal of our research is to provide mechanisms to defend against colluding attackers in the presence of ICA and RA collusion attacks. In this work, we consider a scenario not addressed by previous works, wherein multiple attackers collude against the network, and propose defense mechanisms for such an attack. We take into account the asymmetric nature of social networks and include the case where colluders could add or modify some attributes of their clones. We also consider the case where attackers send few friend requests to uncover their targets. To detect fake reviews and uncovering colluders in crowdsourcing, we propose a semantic similarity measurement between reviews and a community detection algorithm to overcome the non-adversarial attack. ICA in a colluding attack may become stronger and more sophisticated than in a single attack. We introduce a token-based comparison and a friend list structure-matching approach, resulting in stronger identifiers even in the presence of attackers who could add or modify some attributes on the clone. We also propose a stronger RA collusion mechanism in which colluders build their own legitimacy by considering asymmetric relationships among users and, while having partial information of the networks, avoid recreating social circles around their targets. Finally, we propose a defense mechanism against colluding RA which uses the weakest person (e.g., the potential victim willing to accept friend requests) to reach their target

    Investigating emic care in Appalachians of Western North Carolina

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    People in the Appalachian Mountains have an increased prevalence in poverty, low educational attainment, and low employment opportunities that are associated with poor health outcomes. Also, the Appalachian Mountain people suffer stigmas that have been propagated since the late 1890s. However, much of the research has been an etic focus and the emic focus is limited. A qualitative study of emic care explored the ways people in the Appalachian Mountains of Western North Carolina assured wellbeing. Leininger’s Culture of Care model was used to guide the study and discover emic ways of wellbeing. The sample included 21 persons between the ages of 25 and 70 years old, persons who had lived in Western North Carolina (WNC) for 15 years of more and who had generational roots to Appalachia. Individual interviews were conducted in homes, at workplaces and in community settings after consent was obtained. Audiotapes were transcribed verbatim and analyzed through multiple levels to ensure trustworthiness, credibility and validity of findings. Emic themes were identified and included Communal Caring Relationships, Spirituality, Place Matters, Grandmothers Caring, and Etic Care. Specific actions and situations within each theme were reported. For example, the Place Matters theme included participants relating their ingestion of healthy diet of fresh fruits and vegetables from the garden or locally grown (the land). Enjoying the outdoors was related to physical, emotional, and spiritual wellbeing. Spirituality was found to be a major component in a sense of wellbeing, and was described by participants as going to church, not going to church and the old ways. The use of wild crafted herbs and home remedies occurred by the participants in this study. Leininger’s theory and model were useful in guiding the study, as were Spradley’s ethnographic interview guidelines. The well-being discussed by participants provides the emic sense of wellbeing in the Appalachian culture. However, the model was not fully supported in terms of participants’ discussion of challenges or barriers to wellbeing. Rather, the researcher was able to classify participant responses within the areas of physical, spiritual, and mental well-being. Perhaps this is another indication of how etic perspectives focus much of the perceived Appalachian and non-emic health. The findings provide an understanding of well-being and health that can guide future
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