2014 Proceeding- Conference Chairs:
- Alexei Pozdnoukhov,
- Sen Xu
These proceedings contain the papers selected for presentation at the seventh edition of the ACM SIGSPATIAL Workshop on Location-Based Social Networks (LBSN 2014) which is being held in conjunction with the ACM SIGSPATIAL GIS conference in 2014.
Social networks have been prevalent on the Internet and become a hot research topic attracting many professionals from a variety of fields. The advances in location-acquisition and mobile communication technologies empower people to use location data with existing online social networks. The dimension of location helps bridge the gap between the physical world and online social networking services. Furthermore, people in an existing social network can expand their social structure with the new interdependency derived from their locations. As location is one of the most important components of user context, extensive knowledge about an individual's interests, behaviors, and relationships with others can be learned from locations. These kinds of location-embedded and location-driven social structures are known as location-based social networks.
The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of LBSN development and applications, present their ideas and contributions, and set future directions in emerging innovative research for location based social networks. This year program is composed of two tracks of predominantly "social" and "spatial" views on LBSNs.
Proceeding Downloads
From where do tweets originate?: a GIS approach for user location inference
A number of natural language processing and text-mining algorithms have been developed to extract the geospatial cues (e.g., place names) to infer locations of content creators from publicly available information, such as text content, online social ...
VacationFinder: a tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots
Choosing a location for vacations and weekends usually confuses many people. This concern has attracted considerable attention in recent years as currently there is no application based on actual visitors that helps people in finding out the top places ...
Forecasting location-based events with spatio-temporal storytelling
Storytelling, the act of connecting entities through relationships, provides an intuitive platform for exploratory analysis. This paper combines storytelling and Spatio-logical Inference (SLI) to generate rules of interaction among entities and measure ...
Moving on Twitter: using episodic hotspot and drift analysis to detect and characterise spatial trajectories
Today, a tremendous source of spatio-temporal data is user generated, so-called volunteered geographic information (VGI). Among the many VGI sources, microblogged services, such as Twitter, are extensively used to disseminate information on a near real-...
Sophy: a morphological framework for structuring geo-referenced social media
Social networks have played a crucial role of information channels for understanding our daily lives beyond communication tools. In particular, their coupling with geographic location has boosted the worth of social media to detect, track, and predicate ...
WeiboStand: capturing Chinese breaking news using Weibo "tweets"
Weibo is the premier microblog service in China, which is nicknamed as the "Chinese Twitter". Weibo messages consist of text messages, short links, images, audio and video. Its text is restricted to 140 Chinese characters. Since Twitter is blocked in ...
A look into Twitter hashtag discovery and generation
Twitter is a micro-blogging site that allows users and companies to post brief pieces of information called 'Tweets'. Some of the tweets contain keywords such as 'Hashtags' denoted with a '#', essentially one word summaries of either the topic or ...
- Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| LBSN '09 | 15 | 8 | 53% |
| Overall | 15 | 8 | 53% |