A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

An analysis of the user occupational class through Twitter content

Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications

Detecting user demographics in twitter to inform health trends in social media

The widespread and popular use of social media and social networking applications offer a promising opportunity for gaining knowledge and insights regarding population health conditions thanks to the diversity and abundance of online user-generated information (UGHI) relating to healthcare and well-being. However, users on social media and social networking sites often do not supply their complete demographic information, which greatly undermines the value of the aforementioned information for health 2.0 research, e.g., for discerning disparities across population groups in certain health conditions. To recover the missing user demographic information, existing methods observe a limited scope of user behaviors, such as word frequencies exhibited in a user’s messages, leading to sub-optimal results. To address the above limitation and improve the performance of inferring missing user demographic information for health 2.0 research, this work proposes a new algorithmic method for extracting a social media user’s gender by exploring and exploiting a comprehensive set of a user’s behaviors on Twitter, including the user’s conversational topic choices, account profile information, and personal information. In addition, this work explores the usage of synonym expansion for detecting social media users’ ethnicities. To better capture a user’s conversational topic choices using standardized hashtags for consistent comparison, this work additionally introduces a new method that automatically generates standardized hashtags for tweets. Even though Twitter is selected as the experimental platform in this study due to its leading position among today’s social networking sites, the proposed method is in principle generically applicable to other social media sites and applications as long as there is a way to access user-generated content on those platforms. When comparing the multi-perspective learning method with the state-of-the-art approaches for gender classification, a gender classification accuracy is observed of 88.6% for the proposed approach compared with 63.4% performance for bag-of-words and 61.4% for the peer method. Additionally, the topical approach introduced in this work outperforms vocabulary-based approach with a smaller dimensionality at 69.4% accuracy. Furthermore, observable usage patterns of the cancer terms are analyzed across the ethnic groups inferred by the proposed algorithmic approaches. Variations among demographic groups are seen in the frequency of term usage during months known to be labeled as cancer awareness months. This work introduces methods that have the potential to serve as a very powerful and important tool in disseminating critical prevention, screening, and treatment messages to the community in real time. Study findings highlight the potential benefits of social media as a tool for detecting demographic differences in cancer-related discussions on social media

Parallel Processing of Large Graphs

More and more large data collections are gathered worldwide in various IT systems. Many of them possess the networked nature and need to be processed and analysed as graph structures. Due to their size they require very often usage of parallel paradigm for efficient computation. Three parallel techniques have been compared in the paper: MapReduce, its map-side join extension and Bulk Synchronous Parallel (BSP). They are implemented for two different graph problems: calculation of single source shortest paths (SSSP) and collective classification of graph nodes by means of relational influence propagation (RIP). The methods and algorithms are applied to several network datasets differing in size and structural profile, originating from three domains: telecommunication, multimedia and microblog. The results revealed that iterative graph processing with the BSP implementation always and significantly, even up to 10 times outperforms MapReduce, especially for algorithms with many iterations and sparse communication. Also MapReduce extension based on map-side join usually noticeably presents better efficiency, although not as much as BSP. Nevertheless, MapReduce still remains the good alternative for enormous networks, whose data structures do not fit in local memories.Comment: Preprint submitted to Future Generation Computer System