Local experts finding using user comments in location-based social networks

The opinions of local experts in the location-based social network are of great significance to the collection and dissemination of local information. In this paper, we investigated in-depth how the user comments can be used to identify the local expert over social networks. We first illustrate the existences of potential local experts in a social network using a scored model by considering the personal profiles, comments, friend relationship, and location preferences. Then, a multi-dimensional model is proposed to evaluate the local expert candidates and a local expert discovery algorithm is proposed to identify local experts. Meanwhile, a scoring algorithm is proposed to train the weights in the model. Finally, an expert recommendation list can be given based on the score ranks of the candidates. Experimental results demonstrate that effectiveness of proposed model and algorithm

Toward Geo-social Information Systems: Methods and Algorithms

The widespread adoption of GPS-enabled tagging of social media content via smartphones and social media services (e.g., Facebook, Twitter, Foursquare) uncovers a new window into the spatio-temporal activities of hundreds of millions of people. These \footprints" open new possibilities for understanding how people can organize for societal impact and lay the foundation for new crowd-powered geo-social systems. However, there are key challenges to delivering on this promise: the slow adoption of location sharing, the inherent bias in the users that do share location, imbalanced location granularity, respecting location privacy, among many others. With these challenges in mind, this dissertation aims to develop the framework, algorithms, and methods for a new class of geo-social information systems. The dissertation is structured in two main parts: the rst focuses on understanding the capacity of existing footprints; the second demonstrates the potential of new geo-social information systems through two concrete prototypes. First, we investigate the capacity of using these geo-social footprints to build new geo-social information systems. (i): we propose and evaluate a probabilistic framework for estimating a microblog user's location based purely on the content of the user's posts. With the help of a classi cation component for automatically identifying words in tweets with a strong local geo-scope, the location estimator places 51% of Twitter users within 100 miles of their actual location. (ii): we investigate a set of 22 million check-ins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. Concretely, we observe that users follow simple reproducible mobility patterns. (iii): we compare a set of 35 million publicly shared check-ins with a set of over 400 million private query logs recorded by a commercial hotel search engine. Although generated by users with fundamentally di erent intentions, we nd common conclusions may be drawn from both data sources, indicating the viability of publicly shared location information to complement (and replace, in some cases), privately held location information. Second, we introduce a couple of prototypes of new geo-social information systems that utilize the collective intelligence from the emerging geo-social footprints. Concretely, we propose an activity-driven search system, and a local expert nding system that both take advantage of the collective intelligence. Speci cally, we study location-based activity patterns revealed through location sharing services and nd that these activity patterns can identify semantically related locations, and help with both unsupervised location clustering, and supervised location categorization with a high con dence. Based on these results, we show how activity-driven semantic organization of locations may be naturally incorporated into location-based web search. In addition, we propose a local expert nding system that identi es top local experts for a topic in a location. Concretely, the system utilizes semantic labels that people label each other, people's locations in current location-based social networks, and can identify top local experts with a high precision. We also observe that the proposed local authority metrics that utilize collective intelligence from expert candidates' core audience (list labelers), signi cantly improve the performance of local experts nding than the more intuitive way that only considers candidates' locations. ii

Factors required for mobile search going mainstream

This article aims to review the technological and socio-economic conditions which will influence the development of the mobile search market

Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Inferring Degree Of Localization Of Twitter Persons And Topics Through Time, Language, And Location Features

Identifying authoritative influencers related to a geographic area (geo-influencers) can aid content recommendation systems and local expert finding. This thesis addresses this important problem using Twitter data. A geo-influencer is identified via the locations of its followers. On Twitter, due to privacy reasons, the location reported by followers is limited to profile via a textual string or messages with coordinates. However, this textual string is often not possible to geocode and less than 1\% of message traffic provides coordinates. First, the error rates associated with Google\u27s geocoder are studied and a classifier is built that gives a warning for self-reported locations that are likely incorrect. Second, it is shown that city-level geo-influencers can be identified without geocoding by leveraging the power of Google search and follower-followee network structure. Third, we illustrate that the global vs. local influencer, at the timezone level, can be identified using a classifier using the temporal features of the followers. For global influencers, spatiotemporal analysis helps understand the evolution of their popularity over time. When applied over message traffic, the approach can differentiate top trending topics and persons in different geographical regions. Fourth, we constrain a timezone to a set of possible countries and use language features for training a high-level geocoder to further localize an influencer\u27s geographic area. Finally, we provide a repository of geo-influencers for applications related to content recommendation. The repository can be used for filtering influencers based on their audience\u27s demographics related to location, time, language, gender, and ethnicity

Geo Sensitive Word Discovery

Among geolocation related information, in particular, the geo-sensitive word is one of the most critical components. A geo-sensitive word can be a word or phrase for a landmark in the city or county name, abbreviation sports team names in the city, common words or phrases with special meanings in local regions. In this thesis, we propose and evaluate an effective and efficient framework for discovering geo-sensitive words hidden in tweets. This framework overcomes the lack of dataset and embedding alignment problem. There are three key contributions in the proposed framework: (i) a publicly-available dataset containing geo-tagged English tweets from 27 cities in the United States; (ii) a concrete approach to align separately trained word embeddings with Orthogonal Procrustes; (iii) and a well-rounded evaluation framework for geo-sensitive words. The system discovers over 3000 geo-sensitive words in three cities and successfully classified these words into corresponding cities with a 95.32% high accuracy. We also find two key factors that post an impact on the classification performance: (i) feature vector dimension; and (ii) proper learning algorithm

When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning

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Augmenting the performance of image similarity search through crowdsourcing

Crowdsourcing is defined as “outsourcing a task that is traditionally performed by an employee to a large group of people in the form of an open call” (Howe 2006). Many platforms designed to perform several types of crowdsourcing and studies have shown that results produced by crowds in crowdsourcing platforms are generally accurate and reliable. Crowdsourcing can provide a fast and efficient way to use the power of human computation to solve problems that are difficult for machines to perform. From several different microtasking crowdsourcing platforms available, we decided to perform our study using Amazon Mechanical Turk. In the context of our research we studied the effect of user interface design and its corresponding cognitive load on the performance of crowd-produced results. Our results highlighted the importance of a well-designed user interface on crowdsourcing performance. Using crowdsourcing platforms such as Amazon Mechanical Turk, we can utilize humans to solve problems that are difficult for computers, such as image similarity search. However, in tasks like image similarity search, it is more efficient to design a hybrid human–machine system. In the context of our research, we studied the effect of involving the crowd on the performance of an image similarity search system and proposed a hybrid human–machine image similarity search system. Our proposed system uses machine power to perform heavy computations and to search for similar images within the image dataset and uses crowdsourcing to refine results. We designed our content-based image retrieval (CBIR) system using SIFT, SURF, SURF128 and ORB feature detector/descriptors and compared the performance of the system using each feature detector/descriptor. Our experiment confirmed that crowdsourcing can dramatically improve the CBIR system performance