From Interest to Function: Location Estimation in Social Media

Social media plays an increasingly important role in people's life. Microblogging is a form of social media which allows people to share and disseminate real-life events. Broadcasting events in microblogging networks can be an effective method of creating awareness, divulging important information, and so on. However, many existing approaches at dissecting the information content primarily discuss the event detection model and ignore the user interest which can be discovered during event evolution. This leads to difficulty in tracking the most important events as they evolve including identifying the influential spreaders. There is further complication given that the influential spreaders interests will also change during event evolution. The influential spreaders play a key role in event evolution and this has been largely ignored in traditional event detection methods. To this end, we propose a user-interest model-based event evolution model, named the hot event evolution model. This model not only considers the user interest distribution but also uses the short text data in the social network to model the posts and the recommend methods to discover the user interests. This can resolve the problem of data sparsity, as exemplified by many existing event detection methods, and improve the accuracy of event detection. A hot event automatic filtering algorithm is initially applied to remove the influence of general events, improving the quality and efficiency of mining the event. Then, an automatic topic clustering algorithm is applied to arrange the short texts into clusters with similar topics. An improved user-interest model is proposed to combine the short texts of each cluster into a long text document simplifying the determination of the overall topic in relation to the interest distribution of each user during the evolution of important events. Finally, a novel cosine measurebased event similarity detection method is used to assess correlation between events, thereby detecting the process of event evolution. The experimental results on a real Twitter data set demonstrate the efficiency and accuracy of our proposed model for both event detection and user interest discovery during the evolution of hot events.

Protected areas for conservation and poverty alleviation: experiences from Madagascar Charlie J. Gardner*, Martin E. Nicoll, Tsibara Mbohoahy, Kirsten L. L. Oleson, Anitry N. Ratsifandrihamanana, Joelisoa Ratsirarson, Lily-Arison Ren e de Roland, Malika Virah-Sawmy, Bienvenue Zafindrasilivonona and Zoe G. Davies WWF Madagascar and Western Indian Ocean Programme Office, BP738, Antananarivo 101, Madagascar; Durrell Institute of Conservation and Ecology (DICE), School of Anthropology and Conservation, University of Kent, Canterbury, Kent CT2 7NR, UK; D epartement de Biologie, Facult e des Sciences, Universit e de Toliara, Toliara 601, BP 185, Toliara, Madagascar; Blue Ventures Conservation, Level 2 Annex, Omnibus Business Centre, 39-41 North Road, N7 9DP London, UK; ESSA-D epartement Eaux et Forets, Universit e d’Antananarivo, BP 175 Antananarivo, Madagascar; and The Peregrine Fund, BP 4113 Antananarivo, Madagascar

Increasing numbers of smart phone users and Social Network Services (SNS) created a new pattern and characteristic called Social Lifestyle, the way of smartphone users to supply and consume various social media contents. Many SNS services provide individual solutions to group users within a physical location or those who share a common interest in order to exchange and share personal knowledge and contents. Although some of the web-based open platforms are trying to integrate the individual social applications and APIs, there are limitations of sharing social contents and social network information across the separated service providers and platforms. In order to solve such limitations and complexities, there needs to be a generalized model which supports a unified way of social content sharing as well as the system architecture for support the cross social media sharing mechanism. To do so, the paper initially proposes the Place-Content-Social Network based content sharing model (PCS Model) which supports a sophisticated way of connecting user interests for sharing contents within the boundaries of the locations and social network information. The PCS model also provides a social trust for sharing contents based on the analysis of the attributes of the social contents including the user participation, communication patterns and contents sharing activities called the Social Activities.

The interest of users in handheld devices is strongly related to their location. Therefore, the user location is important, as a user context, for news article recommendation in a mobile environment. This paper proposes a novel news article recommendation that reflects the geographical context of the user. For this purpose, we propose the Explicit Localized Semantic Analysis (ELSA), an ESA-based topical representation of documents. Every location has its own geographical topics, which can be captured from the geo-tagged documents related to the location. Thus, not only news articles but locations are also represented as topic vectors. The main advantage of ELSA is that it stresses only the topics that are relevant to a given location, whereas all topics are equally important in ESA. As a result, geographical topics have different importance according to the user location in ELSA, even if they come from the same article. Another advantage of ELSA is that it allows a simple comparison of the user location and news articles, because it projects both locations and articles onto an identical space composed of Wikipedia topics. In the evaluation of ELSA with the New York Times corpus, it outperformed two simple baselines of Bag-Of-Words and LDA as well as two ESA-based methods. Rt10 of ELSA was improved up to 46.25% over other methods, and its [email protected] was always higher than those of the others regardless of k.

Interest based selection of user generated content for rich communication services

Social platforms such as Facebook, Twitter, Instagram, and YouTube have escalated the content published over the internet by the users. Such platforms are considered salient influencing the daily activities causing the various segmentations in the society. The availability of user-generated social media content offers the opportunity to create models capable of extracting and predicting user interests efficiently. Many literature studies attempt the extract user interests to obtain the linage and the co-relation between individuals. However, extracting implicit and future user interests is challenging in research. Therefore, the purpose of this paper is to review the existing methods, the efficiency of extracting and predicting user interests, and to highlight the limitations of the existing methods and techniques. This paper can be beneficial to many research and postgraduate students who focus on the topic related to user personality categorization and, text mining over social media.

PurposeThis paper aims to provide a comprehensive bibliometric analysis of the current state of social media literature by examining co-word network analysis and thematic keyword analysis of both scholars and hospitality and tourism journals in three sub-periods (2002-2006, 2007-2012 and 2013-2018).Design/methodology/approachThis research used a bibliometric analysis of social media literature in the field of hospitality and tourism by synthesizing the literature of a large sample of 601 studies over an extended time period (2002-2018).FindingsThe jump in the number of examined contexts, platforms, methodological approaches and research implications during 2007-2012 has marked the start of social media as a new phenomenon in hospitality and tourism research. Interestingly,International Journal of Contemporary Hospitality Managementwas a leading contributor to social media research between 2017 and 2018. The period 2013-2018 has witnessed newly emerging trends such as “big data,” “e-tourism,” “green experience” and “smart tourism.” This study’s analysis indicated that few keywords in social media appeared in the maturity stage. New platforms such as “Expedia,” “Foursquare,” “Flickr,” “Pinterest,” “Couchsurfing” and “Twitter” appeared between 2013 and 2018.Originality/valueThe scope of past research on the evolution of social media was limited to either a few of the most popular cited journals and/or analysis within a narrow time span. In contrast, the present study aims to uncover the rapid progress in social media research between 2002 and 2018, addressing growth in breadth and depth of thematic areas. Finally, this paper concluded with the proposal of knowledge-based life cycle framework that identifies key themes related to social media research. This framework provided insights into what has been addressed in previous literature (maturity and decline stages) and reported the topics that have been under-researched (introduction and growth stages).

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the Global Positioning System (GPS) is off. Previous research efforts rely on at least one of the two following fundamental requirements, which significantly limit the ability of the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., the device's acceleration, for potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off.

Text similarity is one of the important methods of text data analysis, which is often used in text clustering and classification. Social media is a new and popular online social application that contains a lot of valuable information. Short text is common in social media, and short text similarity is often used for social media data mining. The similarity calculation of short text is influenced by the small feature of text words and the accuracy is low. so it is a common improvement method to calculate the similarity of short texts with word semantic similarity. This paper put forward a short text semantic similarity calculation method that combine knowledge-based method and corpus-based method. This method is based on the improved word semantic similarity calculation method and general short text semantic similarity calculation method. The word similarity calculation method combines two word semantic similarity by some strategies. It takes the advantages of two methods to overcome the disadvantages of single one, finds out more semantic association among words in texts, and improves accuracy of word similarity calculation. This paper uses a large number of corpus to compare and analyze several word and text semantic similarity algorithms, the improved method has a closer result to human ratings than other methods in both word and text similarity.

The online social network services have been growing rapidly over the past few years, and the social network services can easily obtain the locations of users with the recent increasing popularity of the GPS enabled mobile device. In the social network, calculating the similarity between users is an important issue. The user similarity has significant impacts to users, communities and service providers by helping them acquire suitable information effectively. There are numerous factors such as the location, the interest and the gender to calculate the user similarity. The location becomes a very important factor among them, since nowadays the social network services are highly coupled with the mobile device which the user holds all the time. There have been several researches on calculating the user similarity. However, most of them did not consider the location. Even if some methods consider the location, they only consider the physical location of the user which cannot be used for capturing the user's intention. We propose an effective method to calculate the user similarity using the semantics of the location. By using the semantics of the location, we can capture the user's intention and interest. Moreover, we can calculate the similarity between different locations using the hierarchical location category. To the best of our knowledge, this is the first research that uses the semantics of the location in order to calculate the user similarity. We evaluate the proposed method with a real-world use case: finding the most similar user of a user. We collected more than 251,000 visited locations over 591 users from foursquare. The experimental results show that the proposed method outperforms a popular existing method calculating the user similarity.

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

With the rapid development of social networks, short texts have become a prevalent form of social communications on the Internet. Measuring the similarity between short texts is a fundamental task to many applications, such as social network text querying, short text clustering and geographical event detection for smart city. However, short texts in social media always show limited contextual information and they are sparse, noisy and ambiguous. Hence, effectively measuring the distance between short texts is a challenging task.

Location information is very important in the real world. How to detect users' locations and further movement trajectories automatically is significant for many location-based services such as dietary recommendation and tourism planning. With the rapid development of social media such as Sina Weibo and Twitter, more and more people publish posts at any time which contain their real-time location information. This makes it possible to detect users' movement trajectories automatically by social media. In this paper, we propose a novel method to detect city-level locations and further identify movement trajectory for a given user in social media. Our efforts are made in two phases: (1) location detection, and (2) movement trajectory identification. Phase (1) combines two components, i.e. a Bayes model based on words distribution over locations and the social relationships of users in social media, to detect or infer users' locations. The former one is utilized to judge whether the content of a post contains explicit/implicit location information and further to detect the location. The latter one assumes that friends tend to gather together when they talk about the same thing. Thus, it infers a user's locations from his friends' detected locations in social media. Phase (2) constructs the user's movement trajectory by smoothing these detected locations in first phase according to both semantic context of posts and transfer time between locations. Experiments on real dataset from Sina Weibo demonstrate that our approach can outperform baseline methods significantly in terms of Precision, Recall, F1, Error Distance (ErrDist), Detect Ratio (DeteRatio), T-Precision, T-Recall and T-F1, respectively.

Highly dynamic real-time microblog systems have already published petabytes of real-time human sensor data in the form of status updates. However, the lack of user adoption of geo-based features per user or per post signals that the promise of microblog services as location-based sensing systems may have only limited reach and impact. Thus, in this article, we propose and evaluate a probabilistic framework for estimating a microblog user's location based purely on the content of the user's posts. Our framework can overcome the sparsity of geo-enabled features in these services and bring augmented scope and breadth to emerging location-based personalized information services. Three of the key features of the proposed approach are: (i) its reliance purely on publicly available content; (ii) a classification component for automatically identifying words in posts with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. On average we find that the location estimates converge quickly, placing 51% of users within 100 miles of their actual location.

Landscape-aware location-privacy protection in location-based services