Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

Toward Universal Broadband in Rural Alaska

The TERRA-Southwest project is extending broadband service to 65 communities in the Bristol Bay, Bethel and Yukon-Kuskokwim regions. A stimulus project funded by a combination of grants and loans from the Rural Utilities Service (RUS), TERRA-Southwest has installed a middle-mile network using optical fiber and terrestrial microwave. Last-mile service will be through fixed wireless or interconnection with local telephone networks. The State of Alaska, through its designee Connect Alaska, also received federal stimulus funding from the National Telecommunications and Information Administration (NTIA) for tasks that include support for an Alaska Broadband Task Force “to both formalize a strategic broadband plan for the state of Alaska and coordinate broadband activities across relevant agencies and organizations.” Thus, a study of the impact of the TERRA project in southwest Alaska is both relevant and timely. This first phase provides baseline data on current access to and use of ICTs and Internet connectivity in rural Alaska, and some insights about perceived benefits and potential barriers to adoption of broadband. It is also intended to provide guidance to the State Broadband Task Force in determining how the extension of broadband throughout the state could contribute to education, social services, and economic activities that would enhance Alaska’s future. Results of the research could also be used proactively to develop strategies to encourage broadband adoption, and to identify applications and support needed by users with limited ICT skills.Connect Alaska. The National Telecommunications and Information Administration. General Communications Incorporated.Part 1: An Analysis of Internet Use in Southwest Alaska / Introduction / Previous Studies / Current Connectivity / Analytical Framework and Research Methodology / Demographics / Mobile Phones: Access and Use / Access to the Internet / Internet Useage / Considerations about Internet Service / Interest in Broadband / Sources of News / Comparison with National Data / Internet Use by Businesses and Organizations / What Difference may Broadband make in the Region? / Conclusiongs / Part 2 Literature Review / Reference

Mining social network data for personalisation and privacy concerns: A case study of Facebook’s Beacon

This is the post-print version of the final published paper that is available from the link below.The popular success of online social networking sites (SNS) such as Facebook is a hugely tempting resource of data mining for businesses engaged in personalised marketing. The use of personal information, willingly shared between online friends' networks intuitively appears to be a natural extension of current advertising strategies such as word-of-mouth and viral marketing. However, the use of SNS data for personalised marketing has provoked outrage amongst SNS users and radically highlighted the issue of privacy concern. This paper inverts the traditional approach to personalisation by conceptualising the limits of data mining in social networks using privacy concern as the guide. A qualitative investigation of 95 blogs containing 568 comments was collected during the failed launch of Beacon, a third party marketing initiative by Facebook. Thematic analysis resulted in the development of taxonomy of privacy concerns which offers a concrete means for online businesses to better understand SNS business landscape - especially with regard to the limits of the use and acceptance of personalised marketing in social networks

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018