Knowledge Enabled Location Prediction of Twitter Users

As the popularity of online social networking sites such as Twitter and Facebook continues to rise, the volume of textual content generated on the web is increasing rapidly. The mining of user generated content in social media has proven effective in domains ranging from personalization and recommendation systems to crisis management. These applications stand to be further enhanced by incorporating information about the geo-position of social media users in their analysis. Due to privacy concerns, users are largely reluctant to share their location information. As a consequence of this, researchers have focused on automatic inferencing of location information from the contents of a user\u27s tweets. Existing approaches are purely data-driven and require large training data sets of geotagged tweets. Furthermore, these approaches rely solely on social media features or probabilistic language models and fail to capture the underlying semantics of the tweets. In this thesis, we propose a novel knowledge based approach that does not require any training data. Our approach uses Wikipedia, a crowd sourced knowledge base, to extract entities that are relevant to a location. We refer to these entities as local entities. Additionally, we score the relevance of each local entity with respect to the city, using the Wikipedia Hyperlink Graph. We predict the most likely location of the user by matching the scored entities of a city and the entities mentioned by users in their tweets. We evaluate our approach on a publicly available data set consisting of 5119 Twitter users across continental United States and show comparable accuracy to the state-of-the-art approaches. Our results demonstrate the ability to pinpoint the location of a Twitter user to a state and a city using Wikipedia, without needing to train a probabilistic model

The accommodation experiencescape: a comparative assessment of hotels and Airbnb

PURPOSE: Accommodations providers in the sharing economy are increasingly competing with the hotel industry vis-à-vis the guest experience. Additionally, experience-related research remains underrepresented in the hospitality and tourism literature. This paper aims to develop and test a model of experiential consumption to provide a better understanding of an emerging phenomenon in the hospitality industry. In so doing, the authors also expand Pine and Gilmore’s original experience economy construct. DESIGN/METHODOLOGY/APPROACH: Using data from a survey of 630 customers who stayed at a hotel or an Airbnb in the previous three months, the authors performed a multi-step analysis procedure centered on structural equation modeling to validate the model. Findings The authors demonstrate that the dimensions of serendipity, localness, communitas and personalization represent valuable additions to Pine and Gilmore’s original experience economy construct. Airbnb appears to outperform the hotel industry in the provision of all experience dimensions. The authors further define the pathways that underlie the creation of extraordinary, memorable experiences, which subsequently elicit favorable behavioral intentions. PRACTICAL IMPLICATIONS: The findings suggest the need for the hotel industry to adopt a content marketing paradigm that leverages various dimensions of the experience economy to provide customers with valuable and relevant experiences. The industry must also pay greater attention to its use of branding, signage and promotional messaging to encourage customers to interpret their experiences through the lens of these dimensions. ORIGINALITY/VALUE: The study expands a seminal construct from the field of services marketing in the context of the accommodations industry. The Accommodations Experiencescape is offered as a tool for strategic experience design. The study also offers a model of experiential consumption that explains customers’ experiences with accommodations providers

On Internal Knowledge Markets

In large organizations, knowledge can move rapidly or slowly, usefully or unproductively. Those who place faith in internal knowledge markets and online platforms to promote knowledge stocks and flows should understand how extrinsic incentives can crowd outintrinsic motivation

Analysis of Local Experts in Social Media

Recent popular social services (e.g., Foursquare, Twitter, Instagram) are creating a comprehensive geo-social overlay of the planet through geo-located posts, images, and other user-generated content. These public, voluntarily shared footprints provide a potentially rich source for uncovering the landscape of users' interests and topical expertise, which has important implications for social search engines, recommender systems, and other geo and socially-aware applications. This thesis presents the first large-scale investigation of local interests and expertise through an analysis of a unique 13 million user geo-coded list dataset sampled from Twitter. Twitter lists encode a "known for" relationship between a labeler and a labelee. In the small, these lists are helpful for individual users to organize friends or contacts. In the aggregate, however, these lists reveal global patterns of interest and expertise. Concretely, this thesis presents a qualitative and quantitative analysis on the relationships between user locations, interests, and topic expertise as revealed through these Twitter lists. Through thorough analysis this thesis examines the (i) impact of geo-location on topic expertise and users' topic interests in Twitter; (ii) the degree of “locality” of topics; and (iii) the concentration and dispersion of expertise

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

Digital hegemonies: the localness of search engine results

Every day, billions of Internet users rely on search engines to find information about places to make decisions about tourism, shopping, and countless other economic activities. In an opaque process, search engines assemble digital content produced in a variety of locations around the world and make it available to large cohorts of consumers. Although these representations of place are increasingly important and consequential, little is known about their characteristics and possible biases. Analysing a corpus of Google search results generated for 188 capital cities, this article investigates the geographic dimension of search results, focusing on searches such as "Lagos" and "Rome" on different localized versions of the engine. This study answers the questions: To what degree is this city-related information locally produced and diverse? Which countries are producing their own representations and which are represented by others? Through a new indicator of localness of search results, we identify the factors that contribute to shape this uneven digital geography, combining several development indicators. The development of the publishing industry and scientific production appears as a fairly strong predictor of localness of results. This empirical knowledge will support efforts to curb the digital divide, promoting a more inclusive, democratic information society

@Phillies Tweeting from Philly? Predicting Twitter User Locations with Spatial Word Usage

Abstract—We study the problem of predicting home locations of Twitter users using contents of their tweet messages. Using three probability models for locations, we compare both the Gaussian Mixture Model (GMM) and the Maximum Likelihood Estimation (MLE). In addition, we propose two novel unsu-pervised methods based on the notions of Non-Localness and Geometric-Localness to prune noisy data from tweet messages. In the experiments, our unsupervised approach improves the baselines significantly and shows comparable results with the supervised state-of-the-art method. For 5,113 Twitter users in the test set, on average, our approach with only 250 selected local words or less is able to predict their home locations (within 100 miles) with the accuracy of 0.499, or has 509.3 miles of average error distance at best. I

Ecological localness and legitimacy of science policy : mapping climate issue in research over China and Taiwan

As science increasingly plays a vital role in global environmental governance, localness has become the focus in scientific interpretation of nature in terms of policy legitimacy and social communication. However, localness would not germinate until the issue of global change is linked locally, a social construction process which shapes the way of interpreting nature/society interaction and affects local ecological cognition. Localness thus indicates not only legitimacy but also an imperative for responsive actions in a society. It might be argued that although political consideration, with special respect to its stance in international negotiation, determines the extent to which national policy complies with environmental orders set by global regimes, local relevance established in domestic knowledge communication can facilitate policy adjustment. It is therefore crucial for further understanding the emerging and absence of the issues evolved in research agendas. This paper focuses on global warming issues in China and Taiwan for the purpose of mutual reference, and analyzes how ecological localness evolved in the problematization of climate science. Co-word analysis is employed to depict and evaluate the issue structure of the scientific domains and to highlight the relevant local issues which might be taken up by the local scientific community in order to fulfill its accountability as regards place-based communication