Designing Better Location Fields in User Profiles

Social media and Web 2.0 empower individuals to generate content online. It is important to better understand the potential added value of social media for e-health service provision. Social support and credibility of health related information generated via social media is a big challenge for online health communities. In this qualitative research, content of discussions from an online health community is analysed. Two themes are examined: online social support and credibility of online forums. Findings show accuracy and credibility of online communities – user profiles, ratings of posts and improved monitoring of content by advisors improve perceived credibility and trust in online forums and communities. Accuracy and perceived credibility of online health communities is pivotal in facilitating social relationships. While consumers are concerned about the credibility of online information, they benefit from social support and are increasingly turning to social media as a source of information and support. Organizations can benefit from better understanding consumer's use of social media, their concerns about information credibility and need for social support.

Abstract: User profiling on the Web is a topic that has attracted a great number of technological approaches and applications. In most user profiling approaches the website learns profiles from data implicitly acquired from user behaviours, i.e. observing the behaviours of users with a statistically significant number of accesses. This paper presents an alternative approach. In this approach the website explicitly acquires data from users, user interests are represented in a Bayesian network, and user profiles are enriched and refined over time. The profile enrichment is achieved through a sequential asking algorithm based on the value‐of‐information theory using the Shannon entropy concept. However, what mostly characterizes the approach is the fact that the user is involved in a collaborative process of profile building. The approach has been tried out for over a year in a real application. On the basis of the experimental results the approach turns out to be particularly suitable for applications where the website is strongly based on deep domain knowledge (as for example is the case for scientific websites) and has a community of users that share the same domain knowledge of the website and produce a ‘low’ number of accesses (‘low’ compared to the high number of accesses of a typical commercial website). After presenting the technical aspects of the approach, we discuss the underlying ideas in the light of the experimental results and the literature on human–computer interaction and user profiling.

The American cultural landscape has entered an extraordinarily tumultuous time. At the time of this writing, in 2020, a new wave of anti-racist protest is sweeping the globe in response to the murders of unarmed Black women and men both at the hands of the state and white supremacist vigilantes. The memorial landscape, particularly in terms of place names, has become once again a site for (re)asserting a Black sense of place and the past, as well as for diverting attention away from concrete policy demands that could improve the lived material realities of the marginalized, especially Black people. This chapter explores the history and evolution of American place name landscape change and scholarly debates over the politics of place (re)naming. It situates place name politics in a wider critical historical and geographic context by surveying recent struggles over place names on American university campuses. Finally, in view of the increasing role the toponymic landscape plays in oppressed peoples’ demands for change, it concludes with a reflection on the possibilities and limits of reparative place (re)naming and a word of caution given how authorized place name changes are often used to placate rather than meet demands for justice.

There is increasing interest in developing personalized Task-oriented Dialogue Systems (TDSs). Previous work on personalized TDSs often assumes that complete user profiles are available for most or even all users. This is unrealistic because (1) not everyone is willing to expose their profiles due to privacy concerns; and (2) rich user profiles may involve a large number of attributes (e.g., gender, age, tastes, . . .). In this paper, we study personalized TDSs without assuming that user profiles are complete. We propose a Cooperative Memory Network (CoMemNN) that has a novel mechanism to gradually enrich user profiles as dialogues progress and to simultaneously improve response selection based on the enriched profiles. CoMemNN consists of two core modules: User Profile Enrichment (UPE) and Dialogue Response Selection (DRS). The former enriches incomplete user profiles by utilizing collaborative information from neighbor users as well as current dialogues. The latter uses the enriched profiles to update the current user query so as to encode more useful information, based on which a personalized response to a user request is selected. We conduct extensive experiments on the personalized bAbI dialogue benchmark datasets. We find that CoMemNN is able to enrich user profiles effectively, which results in an improvement of 3.06% in terms of response selection accuracy compared to state-of-the-art methods. We also test the robustness of CoMemNN against incompleteness of user profiles by randomly discarding attribute values from user profiles. Even when discarding 50% of the attribute values, CoMemNN is able to match the performance of the best performing baseline without discarding user profiles, showing the robustness of CoMemNN.

Location management schemes are essentially based on users' mobility and incoming call rate characteristics. The network mobility process has to face strong antagonism between its two basic procedures: location update (or registration) and paging. The location update procedure allows the system to keep location knowledge more or less accurately in order to find the user in case of an incoming call, for example. Location registration also is used to bring the user's service profile near its location and allows the network to rapidly provide the user with services. The paging process achieved by the system consists of sending paging messages in all cells where the mobile terminal could be located. A detailed description of the means and techniques for user location management in present cellular networks is addressed.A network must retain information about the locations of endpoints in the network in order to route traffic to the correct destinations. Location tracking (also referred to as mobility tracking or mobility management) is the set of mechanisms by which location information is updated in response to endpoint mobility. In location tracking, it is important to differentiate between the identifier of an endpoint (i.e., what the endpoint is called) and its address (i.e., where the endpoint is located). Mechanisms for location tracking provide a time-varying mapping between the identifier and the address of each endpoint. Most location tracking mechanisms may be perceived as updating and querying a distributed database (the location database) of endpoint identifier-to-address mappings. In this context, location tracking has two components: (1) determining when and how a change in a location database entry should be initiated; and (2) organizing and maintaining the location database. In cellular networks, endpoint mobility within a cell is transparent to the network, and hence location tracking is only required when an endpoint moves from one cell to another. Location tracking typically consists of two operations: (1) updating (or registration), the process by which a mobile endpoint initiates a change in the location database according to its new location; and (2) finding (or paging), the process by which the network initiates a query for an endpoint's location (which also may result in an update to the location database). Most location tracking techniques use a combination of updating and finding in an effort to select the best trade-off between update overhead and delay incurred in finding. Specifically, updates are not usually sent every time an endpoint enters a new cell, but rather are sent according to a predefined strategy so that the finding operation can be restricted to a specific area. There is also a trade-off, analyzed formally between the update and paging costs.Location management methods are most adapted and widely used in current cellular networks, e.g., GSM, IS-54. IS-95, etc. The location management methods are broadly classified into two groups. The first group includes all methods based on algorithms and network architecture, mainly on the processing capabilities of the system. The second group contains the methods based on learning processes, which require the collection of statistics on subscribers' mobility behavior, for instance.

Community Question‐Answering (CQA) sites have become popular venues where people can ask questions, seek information, or share knowledge with a user community. Although responses on CQA sites are obviously slower than information retrieved by a search engine, one of the most frustrating aspects of CQAs occurs when an asker's posted question does not receive a reasonable answer or remains unanswered. CQA sites could improve users' experience by identifying potential answerers and routing appropriate questions to them. In this paper, we predict the potential answerers based on question content and user profiles. Our approach builds user profiles based on past activity. When a new question is posted, the proposed method computes scores between the question and all user profiles to find the potential answerers. We conduct extensive experimental evaluations on two popular CQA sites ‐ Yahoo! Answers and Stack Overflow ‐ to show the effectiveness of our algorithm. The results show that our technique is able to predict a small group of 1000 users from which at least one user will answer the question with a probability higher than 50% in both CQA sites. Further analysis indicates that topic interest and activity level can improve the correctness of our approach.

Velocity of data generation has increased over a period of decade which is expected to further increase exponentially with the passage of time. To mine the useful nuggets of information, satisfying a large community of users it is preferred to capture the interest of the user, i.e., to create a user profile, and then filter the content according to his taste. A user may traverse through a large number of documents, requiring a user profiling technique to support the scalability of growing number of documents. This paper proposes a novel technique of user profiling - Map Reduce based Vector Space Model (MR-VSM). MR-VSM is a technique for user profiling where the user interacts with data rich in text and volume. MR-VSM implements traditional VSM to use Map Reduce, a parallel programming paradigm to increase the computational efficiency and support scalability of documents. It works by parallelizing the task of creating a termdocument class of VSM by using TF-IDF to create term vector. For experimental study this paper makes use of the News dataset which is rich in text and volume and is collected from the web using RSS feeds. The proposed system creates user profile by taking into consideration the News item read by the user and creating a term vector for each news item read. Resulting user profile is set of Top-n terms. To test the computational efficiency and scalability of MR-VSM for growing number of news items read by user, MR-VSM is made to run on a cluster of Hadoop for 12,000, 24,000 and 48000 news items. VSM is also run for 1,500 news items to show the computational efficiency of the proposed approach. It is observed that for MR-VSM computational time for user profiling and scalability of news item read by the user are improved with the increase in the number of nodes in a Hadoop cluster.

Introduction For the past several decades forests in the entire Hindu Kush-Himalaya belt are under severe threat of overexploitation. The natural coniferous forests of the Hilkot watershed in the Pakistani Hindu Kush-Himalayas are no exception, and the impact of this threat can be felt in various ways. A complex relationship exists, here as elsewhere, between the forest and the user communities--notably their economies. The ever-increasing population of mountain communities, particularly those who are directly dependent on the forests, along with their growing needs for timber, firewood and fodder, have led to immense pressure on forests (Asghar 1984: 22-23). The user communities in Hilkot consist of various ethnic groups, most of which are hierarchically structured--structures which tend to hinder in finding solutions to the problems of deforestation. One of these communities is that of the Gujars, and this paper deals briefly with the conflicts that arise in the context of their use of the forest. The Gujars in the Mansehra District are often attributed a significant portion of blame for the destruction of the forests in this area. As cattle breeders and herders, their way of life is intimately connected to, and relevant for, the measures that must be taken towards forest conservation. Being poor and of a low social status, they are heavily dependent on the forests (Asghar 1984: 25), and since they also comprise a large percentage of the population, the community as a whole has a major impact on the forests. Thus, several questions need to be asked: which particular uses of the forest are associated with the Gujar way of life? to what extent are they dependent on these? and in which way do these conflict with other uses of these forests? Unfortunately, no studies have been carried out to date among the Gujars in this area. This report thus aims at providing some basic information about their economy, with special emphasis on the role of the forest in their lifestyle. The Distribution of the Gujars In Hilkot, as in the entire District of Mansehra, Gujars represent the largest ethnic group. Though little is known with any certainty about their origins, they have been said to be the original inhabitants of this area (Watson 1907: 62-24). It has also been suggested that they settled mainly in the Punjab at the beginning of sixth century (Ibbetson and Maclagan 1892: 112-13), and the similarities between their language, Gujri, and Punjabi indicate that their roots are in Punjab. Some place names in Punjab, such as Gujrat and Gujranwala, also appear to support this statement. In the seventh century, the Gujar probably began to settle in the mountain valleys in the northwest of the Indian subcontinent (Ibbetson and Maclagan 1982: 33-37). Today their mountain settlements extend from the Central Indian Himalayas (the Indian state of Uttar Pradesh) in the east to the Hindukush in the west. Gujar Economic Systems and Way of Life The Gujars largely practise seasonal and vertical nomadism, which has, however, evolved over time into several, partly overlapping, ways of life (Mohammad 1989: 126-27). Each of these overlapping sub-systems depends largely on cattle raising. Vertical nomadism is practised by most of the Gujars in this area. It is characterised by the mobility of the entire household, which spends the winter season in the foothills with their sheep, goats and buffalo herds and moves early in summer to the extensive alpine pastures between the Himalayas and the Hindukush. As opposed to such vertical nomadism, those Gujars who live in the valley bottom and own some land there, or work as tenants, practise a kind of transhumance. After sowing maize on their land, they leave their houses early in summer and shift to their banda--individual farms on the higher slopes that receive the monsoon rains (Fautz 1963: 123-29). Here they again sow some maize and herd their livestock in the forest. …

Social network sites, like Facebook, Tweeter and Flickr provide users the opportunity to share their media content, such as videos, music tracks or photos. Beyond the fact that they can share information the users can also vote or comment on information posted by other users. Social networks take advantage of this activity and create groups and communities of users with similar interests. This categorization helps social network systems to support users with data, e.g. videos, photos or users profiles, that are relevant to their interests. In order to increase the effectiveness of navigation, analysis of social media content graphs needs to be done. In this paper, an analysis of the Youtube social media graph is presented. Graphs of 2D and 3D videos are considered in this analysis. Well known properties of web and social networks analysis, like the power-law distribution are discussed. Moreover, clustering methods are applied in order to study the existence of media content groups. Finally, the results of our analysis are discussed and directions of future work are presented.

Web 2.0 streams, like blog postings, micro-blogging tweets, or RSS feeds from online communities, offer a wealth of latest news about real-world events and societal discussion. From a user’s perspective, it becomes harder and harder to get a decent overview of recent events, given these massive streams of information that are continuously flowing. Ideally, a system would continuously put together recent information, ranked by the current social impact but also weighted by the users’ personal interests. In this work, we develop methods to meet these requirements. The presented approach continuously tracks the most popular tags attached to the incoming items and based on this, constructs a dynamic top-k query. By continuous evaluation of this query on the incoming stream, we are able to retrieve the currently hottest items. These hottest items are then fed into an engine that re-ranks them w.r.t. user specified interests, given in form of term based topic descriptions. This calls for high performance algorithms for efficient hot document retrieval and subsequently personalizing these documents based on user profiles, given the high rate of incoming data and the immense number of user profiles. In this work we present a combined solution, making use of our prior work on information filtering and showing how it can be used in combination with the current work, on how to continuously determine the hottest documents. To demonstrate the suitability of our approach, we perform a performance evaluation using a real-world dataset obtained from a weblog crawl.

One of the implications of aging population is the increasing need for services for elderly people, with an emphasis on earlier diagnosis and effective interventions promoting an active and healthy aging process. To face this need, the MoveCare Project develops and field-tests an innovative multi-actor platform that integrates a robotic system with environmental sensors, smart objects, a virtual community and an activity center to provide assistance, transparent monitoring and activities to the elder at home. The newness of MoveCare consists in the design of a transparent monitoring and in its social and overarching nature that envisages the cooperation of multiple actors to implement a well-accepted, personalized, and engaging platform to hinder decline in independent elderly users. This work illustrates the rationale and the implementation of transparent physical monitoring during the execution of balancetraining exergames tailored to the user performance. Different game modalities, such as playing alone, in competition, or collaboration with other elders of the virtual community will be available. Real-time and offline indicators will be extracted and analyzed to keep track of the user's profile with the aim of preventing physical and social decline.

One form of online misbehaviour which has deeply affected society with harmful consequences is known as cyberbullying. Cyberbullying can simply be defined as an intentional act that is conducted through digital technology to hurt someone. Cyberbullying is a widely covered topic in the social sciences. There are many studies in which the problem of cyberbullying has been introduced and its origins and consequences have been explored in detail. There are also studies which have investigated the intervention and prevention strategies and have proposed guidelines for parents and adults in this regard. However, studies on the technical dimensions of this topic are relatively rare. In this research the overall goal was to bridge the gap between social science approaches and technical solutions. In order to be able to suggest solutions that could contribute to minimizing the risk and impact of cyberbullying we have investigated the phenomenon of cyberbullying from different angles. We have thoroughly studied the origin of cyberbullying and its growth over time, as well as the role of technology in the emergence of this type of virtual behaviour and in the potential for reducing the extent of the social concern it raises. First we introduced a novel outlook towards the cyberbullying phenomenon. We looked into the gradual changes which have occurred in relationships and social communication with the emergence of the Internet. We argued that one should look at virtual environments as virtual communities, because the human needs projected on these environments, the relationships, human concerns and misbehaviour have the same nature as in real-life societies. Therefore, to make virtual communities safe, we need to take safety measures and precautions that are similar to the ones that are common in non-virtual communities. We derived the assumption that if cyberbullying is recognized and treated as a social problem and not just seen as some random mischief conducted by individuals with the use of technology, the methods for handling its consequences are likely to be more realistic, effective and comprehensive. This part of our study led to the conviction that for combating cyberbullying, behavioural and psychological studies, and the study of technical solutions should go hand in hand. One of the main limitations that we faced when we started our research was the lack of a comprehensive dataset for cyberbullying studies. We needed a dataset which included real instances of bullying incidents. Moreover, it was essential for our studies to also have the demographic information of the social media users as well as the history of their activities. We started our preliminary experiments using a dataset that was collected from MySpace forums. This dataset did not meet all the requirements for our experiment, namely in terms of size and sufficiency of information. Therefore we developed our own YouTube dataset, with the aim to encompass extensive information about the users and their activities as well as larger numbers of bullying comments. We collected information on user activities and posted textual comments as well as personal and demographic details of the users involved. Detecting a bullying comment or post at the earliest possible moment in time can substantially decrease the negative effects of cyberbullying incidents. We started our experiments by showing that besides the conventional features used for text mining methods such as sentiment analysis and specifically bullying detection, more personal features, in this experiment gender, can improve the accuracy of the detection models. As expected the models which were optimized accordingly resulted in a more accurate classification. The improved outcome motivated us to look into other personal features as well, such as age and the writing style of users. By adding more personal information, the previous classification results were outperformed and the detection accuracy enhanced even further. In the last experiment we made use of experts’ knowledge to identify potential bully users in social networks. To better understand and interpret the intentions underlying the online activities of users of social media, we decided to incorporate human reasoning and knowledge into a bulliness rating system by developing a Multi-Criteria Evaluation System. Moreover, to have more sources of information and to make use of the potential of both human and machine, we designed a hybrid approach, incorporating machine learning models on top of the expert system. The hybrid approach reached an optimum model which outperformed the results obtained from the machine learning models and the expert system individually. Our hybrid model illustrates the added value of integrating technical solutions with insights from the social sciences for the first time. As argued in this thesis, the integration of social studies into a software-enhanced monitoring workflow could pave the way towards the tackling of this kind of online misbehaviour. The ideas and algorithms proposed for fulfilling this purpose can be a stepping stone for future research in this direction. The work carried out is also a demonstration of the added value of frameworks for text categorization, sentiment mining and user profiling in applications addressing societal issues. This work can be viewed as a contribution to the more general societal challenge of increasing the level of cybersecurity, in particular for the younger generations of social network users. By turning the internet into a safer place for children, the chances increase that they will be able to benefit from the informational richness that it also offers.

This chapter presents an agent-based interface for business-to-customer (B2C) applications that is not merely reactive to some user request, but proactive and capable of engaging in a goal-directed conversation with the user, e.g. by taking the initiative to recommend new products. The approach combines content-based filtering, where user profiles are generated based on content features extracted from documents that users find relevant, with collaborative filtering which clusters users according to their expressed taste to generate recommendations within these virtual communities. In addition, the naturalness of interaction, especially for casual users, is enhanced by appropriate 2D and 3D animations that express the agent’s emotional reactions.

Advances in Web 2.0 technologies have led to unstructured information overload in the complex and multidimensional datasets that originate in social networks. This overload can diminish the quality of web-related services such as recommender systems in the social web and can overwhelm users with irrelevant information. Such overload indicates the need to design and develop efficient and accurate information management and retrieval systems in social networks and anticipate recommendations in the semantic context of user interest. Community detection in social networks can help identify higher-order structures that unveil insight into networks and their functional organization and reduce irrelevant information received by the user. In this study, the application of community detection in online social networks is investigated within the framework of topic discovery-based user link identification and is consequently used to uncover implicit user communities (ad hoc communities). In the proposed approach, we use a graph-based information extraction technique that provides for a personalized information retrieval (recommender) system. We hypothesize that the ad hoc communities of users sharing similar interests embedded in a folksonomy-based social network can be identified by overlapping tag clusters in the tag concept hierarchy. Our methodology incorporates the novel information extraction techniques of topic modeling for topic extraction (feature extraction), user profile modeling for user profile extraction, and community extraction from the social graph, modeled in a framework to derive relevant ad hoc user communities of social interest from folksonomy data. Our experimental results demonstrate an accuracy of 70–98 % in community detection using data obtained from CiteULike®.

XML ( eXtensible Markup Language ) has emerged as a prevalent standard for document representation and exchange on the Web. It is often the case that XML documents contain information of different sensitivity degrees that must be selectively shared by (possibly large) user communities. There is thus the need for models and mechanisms enabling the specification and enforcement of access control policies for XML documents. Mechanisms are also required enabling a secure and selective dissemination of documents to users, according to the authorizations that these users have. In this article, we make several contributions to the problem of secure and selective dissemination of XML documents. First, we define a formal model of access control policies for XML documents. Policies that can be defined in our model take into account both user profiles, and document contents and structures. We also propose an approach, based on an extension of the Cryptolope™ approach [Gladney and Lotspiech 1997], which essentially allows one to send the same document to all users, and yet to enforce the stated access control policies. Our approach consists of encrypting different portions of the same document according to different encryption keys, and selectively distributing these keys to the various users according to the access control policies. We show that the number of encryption keys that have to be generated under our approach is minimal and we present an architecture to support document distribution.