The Spanish Revolution in Twitter (1): Hashtags, Escraches and Anti – Evictions social movement in Spain

Old Revolutions and Social Movements used to be diffused to the public with the help of a combination of meetings, assemblies, and also through instruments as pamphlets, posters, by word of mouth, and similar. One very important change began at the beginning of the first decade of the twenty-first century, when Web 2.0 based on the developing of Social Networks through the Internet introduced new ways of announce or call any type of protest, meeting, etc. introducing the diffusion by very effective and fast means, on real-time, as Twitter. We analize the use of the hashtag “SpanishRevolution” from all the tweets published in Twitter from 10 April 2013 to 28 May 2013; describe the main other hashtags included in the tweets in which the hashtag “SpanishRevolution” was found; discover the connections between this and other hashtags included in the same tweets, looking for patterns in the micro discourses produced by the hashtags; and determine the patterns and types of hashtags included in the tweets, that is, are the hashtags alluding to slogans, places, people, or to what?

What Happen After Crawling Big Data?

We test a methodology to automatically filtering, coding and reducing the huge amount of data retrieved from Twitter, as a previous task to be done before the analysis of Big Data, and to determine the reliability of the methodology after being applied to a dataset of 500,000 tweets on the ‘desahucios’ (evictions) thematic. We explain the process followed to achieve these tasks. Basically, we extracted a random sample of 1,000 clusters from a dataset of 500,000 tweets around the ‘desahucios’ thematic that was retrieved from 10 April 2013 to 28 May 2013 period. Hashtags on this sample were automatically filtered, codified and reduced according to the Levenshtein distance metric.[1] Different automatic algorithms were applied to the 100,000 sample of tweets for filtering, coding and reducing the number of hashtags. After this operation, a new statistically representative sample of hashtags was selected in order to determine the reliability of the automatic algorithm created. In this last step two researchers manually checked case by case if the hashtags were correctly clustered. Results present all the process and the evaluation of the best algorithm for reducing twitter data on the eviction thematic.

What happen after crawling big data? from jcarpioc

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[1] Informally, the Levenshtein distance between two words is the minimum number of single-character edits (i.e. insertions, deletions or substitutions) required to change one word into the other. http://en.wikipedia.org/wiki/Levenshtein_distance (retrieved 13.03.2014).

Production of New Knowledge Through Automated Big Data Extraction from Social Bookmarking Systems

Social tagging systems have gained increasing popularity as a method of annotating and categorising a wide range of different web resources. We use big data from Web 2.0 in social research to discover some type of structuration around the issue of the globalisation of agriculture and, particularly, within Delicious. We retrieved a sample of 3,668 users, 2,148 URLs and 4,776 tags, and through social network analysis (SNA), we found out what types of URLs around our topic have been recommended via collaborative tagging, what types of actors label URLs around this topic, whether there is some type of structuration and hierarchy to be discovered in the network of the globalisation of agriculture (centrality, substructures, etc.), and what types of tags actors are using to specifically label (and thus define and qualify) the URLs on the globalisation of agriculture that they recommend through Delicious.

What's happening in Spain?

Institutional change can create opportunities for potential entrepreneurs by shaping and determining the prospects as well as removing or lowering barriers to market entry and/or exit and thus can exert a positive impact on entrepreneurial leadership (Gnyawali and Fogel 1994; Hwang and Powell 2005; Smallbone and Welter 2001). But I think, that's not true in the spanish case.

What's the problem with entrepreneurship? Are the regulatory institutions? Is the contextual embeddedness of entrepreneurship? Are the entrepreneurs?