Microblogging and nanotweets: Nanotechnology on Twitter

Abstract

The social web represents a new arena for local, national and global conversations and will play an increasing role in the public understanding of science. This paper presents an analysis of the representations of nanotechnology on Twitter, analysing over 24,000 tweets in terms of web metrics, latent semantic and sentiment analysis. Results indicate that most active users on nanotechnology are distributed according to a power law distribution and that web metric indicators suggest little conversation on the topic. In terms of content, there is a remarkable similarity with previous studies of nanotechnology's representations in other media outlets. Related to content is the sentiment analysis that indicates predominantly positively loaded words in the corpus.Negative sentiments mainly took the form of uncertainty and fear of the unknown rather than open hostility.

Measuring international relations in social media conversations

“…In a network based on co-occurrence, high centrality means a high degree of salience and visibility of a semantic concept or entity in conversations (Doerfel & Connaughton, 2009). Various prior studies have used this approach to identify import themes in public discussions online (Oh, Kwon, & Rao, 2010;Veltri, 2012). Accordingly, in a network based on co-occurrence of country names, countries with a high degree of centrality are considered highly salient and visible.…”

CyberSociety and public debate in 140 characters: # unemployment and # corruption

“…De hecho, varias investigaciones han afirmado que Twitter es un espacio para compartir información, para hablar, pero no para escuchar, un entorno donde se desarrolla un proceso de comunicación unidireccional y no bilateral (Lovejoy, Waters y Saxton, 2012;Waters y Jamal, 2011), registrando escasas conversaciones (Veltri, 2014).…”

A Study on the Influence of Semantics on the Analysis of Micro-blog Tags in the Medical Domain

“…visualization of clusters of similar tweets [14,15], recommendation of hashtags [16], sentiment analysis [17,18], detection of events or common topics [19,20], clustering of tweets [21,22], etc.). However, in most cases it can be observed that the treatment of the tweet components (mainly the words contained in the body of the tweet and its hashtags) is purely syntactic.…”

“…Mathiesen et al [24] build graphs of terms, where the edges are weighted by their co-occurrence, and study the communities present in these graphs. Veltri [21] also uses the co-occurrence between words to classify tweets related to the Nanotechnology domain. The lack of a semantic treatment of the content of the tweets is the main shortcoming of all these approaches.…”