Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election

Tsou, Ming‐Hsiang; Yang, Jiue-An; Lusher, Daniel; Han, SangUk; Spitzberg, Brian H.; Gawron, Jean Mark; Gupta, Dipak Kumar; An, Li

doi:10.1080/15230406.2013.799738

Cited by 98 publications

(54 citation statements)

References 21 publications

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“…In addition, Cheng and Wicks [15] employed space-time scan statistics to detect statistically significant space-time events. Using the 2012 US Presidential Election candidates as a case study, Tsou et al [16] analyzed the spatial distribution of web pages and related social media (Twitter) messages, which can facilitate the tracking of ideas and social events disseminated in cyberspace from a spatial-temporal perspective.…”

Section: Introductionmentioning

confidence: 99%

Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm

Wang

et al. 2015

Sustainability

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Abstract:With the proliferation of social media, information generated and disseminated from these outlets has become an important part of our everyday lives. For example, this type of information has great potential for effectively distributing political messages, hazard alerts, or messages of other social functions. In this work, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text streams during emergency events. This model classifies social media text streams based on their topical contents. Integrated with a trend analysis, we show how Sina-Weibo fluctuated during emergency events. Using a spatial statistical analysis method, we found that the distribution patterns of Sina-Weibo were related to the emergency events but varied among different topics. This study helps us to better understand emergency events so that decision-makers can act on emergencies in a timely manner. In addition, this paper presents the tools, methods, and models developed in this study that can be used to work with text streams from social media in the context of disaster management and urban sustainability.

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Section: Introductionmentioning

confidence: 99%

Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm

Wang

et al. 2015

Sustainability

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“…APIs, offered by some popular web content providers or social network sites such as Flickr and Twitter [20][21][22][23]43], allow users' programs to automatically obtain geo-tagged web resources and can be readily integrated into users' programs [21]. For instance, Li et al [20] use Twitter's and Flickr's public APIs to collect tweets and photos from 21 January 2011 to 7 March 2011 within the bounding box of the contiguous United States for discovering sociolinguistic associations.…”

Section: Geo-tagged Web Resources Collectionmentioning

confidence: 99%

Active Collection of Land Cover Sample Data from Geo-Tagged Web Texts

Hou

Chen

et al. 2015

Remote Sensing

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Sample data plays an important role in land cover (LC) map validation. Traditionally, they are collected through field survey or image interpretation, either of which is costly, labor-intensive and time-consuming. In recent years, massive geo-tagged texts are emerging on the web and they contain valuable information for LC map validation. However, this kind of special textual data has seldom been analyzed and used for supporting LC map validation. This paper examines the potential of geo-tagged web texts as a new cost-free sample data source to assist LC map validation and proposes an active data collection approach. The proposed approach uses a customized deep web crawler to search for geo-tagged web texts based on land cover-related keywords and string-based rules matching. A data transformation based on buffer analysis is then performed to convert the collected web texts into LC sample data. Using three provinces and three municipalities directly under the Central Government in China as study areas, geo-tagged web texts were collected to validate artificial surface class of China's 30-meter global land cover datasets (GlobeLand30-2010). A total of 6283 geo-tagged web texts were collected at a speed of 0.58 texts per second. The collected texts about built-up areas were transformed into sample data. User's accuracy of 82.2% was achieved, which is close to that derived from formal expert validation. The OPEN ACCESSRemote Sens. 2015, 7 5806 preliminary results show that geo-tagged web texts are valuable ancillary data for LC map validation and the proposed approach can improve the efficiency of sample data collection.

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“…Reference librarians can provide assistance as professionals to numerous searchers [12]. The social search asserts that (a) social network links can be leveraged to improve the quality of search results, and that (b) a growing body of Internet content cannot be retrieved by traditional web search as it is not wellconnected to the hyperlinked Web [14], [13]. It is said that current web search engines are not able to find relevant information available on online social networks.…”

Section: A Social Searchmentioning

confidence: 99%

Empirical Evaluation of Social and Traditional Search Tools for Adhoc Information Retrieval

Hussain¹,

Missen²,

Akhtar³

et al. 2017

ijacsa

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Abstract-The nature of World Wide Web (www) has evolved over the passage of time. Easier and faster availability of Internet has given rise to huge volumes of data available online. Another cause of huge volumes of data is the emergence of online social networks (like Facebook, Twitter, etc.) which has actually changed the role of data consumers to data generators. Increasing popularity of these online social networks has also changed the way different web services used to be used. For example, Facebook messaging has some impact on usage of emails; twitter usage affects (positively or negatively) online newspaper readings. Both of these platforms are heavily used for information searching. In this paper, we evaluate the role of Facebook and Twitter for academic queries and compare the findings with Google search engines to find out if there is a chance that these online social networks will replace Google sooner. A query set selected from the standard AOL dataset is used for experimentation. Academic related queries are selected and classified by expert users. Findings of Google, Facebook and Twitter are compared against these queries using Mean Average Precision (MAP), as a metrics for evaluation. Results conclude that Google has the dominating factor with a better MAP than Facebook and Twitter.

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Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election

Cited by 98 publications

References 21 publications

Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm

Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm

Active Collection of Land Cover Sample Data from Geo-Tagged Web Texts

Empirical Evaluation of Social and Traditional Search Tools for Adhoc Information Retrieval

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