Square PoliticsKey characteristics of the indignant mobilizations in Greece.

This paper discusses the findings from an online survey completed by nearly 500 persons claiming participation in the indignant (Aganaktismenoi) mobilizations of Syntagma square in Athens during May/June 2011. The demographics of the respondents could have been highly affected by the research medium that was used. However, this paper argues that since the indignant mobilizations were called across different nations by using online social networks, like facebook, the characteristics identified in the Greek case perfectly fit within the general pattern that characterised the participants in these mobilizations. As such, this paper puts the mobilizations at Syntagma square in a good footing for comparative cross-national examination. Furthermore, this paper confirms the increasingly important role played by cyber activism over socio-political contestation in the Greek context. In addition, it discusses the impact that this cyber activism has on the gender composition of political activism and the role of mainstream political participation.

Use and views on social networking sites of pharmacy students in the United Kingdom

Objective. To investigate students' use and views on social networking sites and assess differences in attitudes between genders and years in the program.

Methods. All pharmacy undergraduate students were invited via e-mail to complete an electronic questionnaire consisting of 21 questions relating to social networking.

Results. Most (91.8%) of the 377 respondents reported using social networking Web sites, with 98.6% using Facebook and 33.7% using Twitter. Female students were more likely than male students to agree that they had been made sufficiently aware of the professional behavior expected of them when using social networking sites (76.6% vs 58.1% p=0.002) and to agree that students should have the same professional standards whether on placement or using social networking sites (76.3% vs 61.6%; p<0.001).

Conclusions. A high level of social networking use and potentially inappropriate attitudes towards professionalism were found among pharmacy students. Further training may be useful to ensure pharmacy students are aware of how to apply codes of conduct when using social networking sites.

Are Computer Scientists Geeks? Gender Differences In Attitudes And Experiences Related To Computing Among Secondary Education Students In Northern Ireland.

The worldwide scarcity of women studying or employed in ICT, or in computing related disciplines, continues to be a topic of concern for industry, the education sector and governments. Within Europe while females make up 46% of the workforce only 17% of IT staff are female. A similar gender divide trend is repeated worldwide, with top technology employers in Silicon Valley, including Facebook, Google, Twitter and Apple reporting that only 30% of the workforce is female (Larson 2014). Previous research into this gender divide suggests that young women in Secondary Education display a more negative attitude towards computing than their male counterparts. It would appear that the negative female perception of computing has led to representatively low numbers of women studying ICT at a tertiary level and consequently an under representation of females within the ICT industry. The aim of this study is to 1) establish a baseline understanding of the attitudes and perceptions of Secondary Education pupils in regard to computing and 2) statistically establish if young females in Secondary Education really do have a more negative attitude towards computing.

Improved lexicon-based sentiment analysis for social media analytics

Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public attitude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for different organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key components, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classification process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.

Scoup-SMT: Scalable Coupled Sparse Matrix-Tensor Factorization

How can we correlate neural activity in the human brain as it responds to words, with behavioral data expressed as answers to questions about these same words? In short, we want to find latent variables, that explain both the brain activity, as well as the behavioral responses. We show that this is an instance of the Coupled Matrix-Tensor Factorization (CMTF) problem. We propose Scoup-SMT, a novel, fast, and parallel algorithm that solves the CMTF problem and produces a sparse latent low-rank subspace of the data. In our experiments, we find that Scoup-SMT is 50-100 times faster than a state-of-the-art algorithm for CMTF, along with a 5 fold increase in sparsity. Moreover, we extend Scoup-SMT to handle missing data without degradation of performance. We apply Scoup-SMT to BrainQ, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. Scoup-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy. Finally, we demonstrate the generality of Scoup-SMT, by applying it on a Facebook dataset (users, friends, wall-postings); there, Scoup-SMT spots spammer-like anomalies.

Twitter Sentiment Analysis for Security-Related Information Gathering

Analysing public sentiment about future events, such as demonstration or parades, may provide valuable information while estimating the level of disruption and disorder during these events. Social media, such as Twitter or Facebook, provides views and opinions of users related to any public topics. Consequently, sentiment analysis of social media content may be of interest to different public sector organisations, especially in the security and law enforcement sector. In this paper we present a lexicon-based approach to sentiment analysis of Twitter content. The algorithm performs normalisation of the sentiment in an effort to provide intensity of the sentiment rather than positive/negative label. Following this, we evaluate an evidence-based combining function that supports the classification process in cases when positive and negative words co-occur in a tweet. Finally, we illustrate a case study examining the relation between sentiment of twitter posts related to English Defence League and the level of disorder during the EDL related events.