Incorporating social role theory into topic models for social media content analysis

In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo! Answers, where social roles on Twitter include "originators" and "propagators", and roles on cQA are "askers" and "answerers". Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author's research expertise area is considered as a social role. A novel application of detecting users' research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model.

Motherhood on ice? A media framing analysis of older mothers in the UK news

Changing gender roles and increased sexual and economic freedom have created opportunities for women to give birth relatively late in life. However, stigma and misplaced fears about physical capacity are often reported as sources of anxiety among older, and in vitro fertilisation-induced mothers. In this study, we apply a specially adapted method for analysing news media content to a week's selection of material in the British media following the dissemination of research at an international medical conference. Our findings suggest, despite some positive commentaries, that much negative discourse is circulated by the media about older mothers, from implied claims of selfishness (older mothers as 'delaying' conception) to violations of the 'natural order'. These latter claims reflect the long-standing ambivalence by the media generally towards scientific advancement, but they also reveal continuing resistance towards unorthodox lifestyles.

"Crack down on the celebrity junkies": does media coverage of celebrity drug use pose a risk to young people?

This study analysed news media content to examine the role played by celebrity drug use in young people's perceptions of drug use. We know that young people have access to discourses of drug use through music and other media which may emphasise short term gains (of pleasure or sexual success) over longer term health and social problems. This study goes beyond a simple modelling approach by using Media Framing Analysis (MFA) to take an in-depth look at the messages themselves and how they are 'framed'. New stories about Amy Winehouse's drug use were used and we conducted focus groups with young people asking them questions about drugs, celebrity and the media. Frames identified include: 'troubled genius', 'losing patience' and 'glamorization or gritty realism'. Initially, the press championed Winehouse's musical talent but soon began to tire of her recklessness; the participants tended to be unimpressed with Winehouse's drug use, characterising her as a promising artist who had 'gone off the rails'. Young people were far more critical of Winehouse than might be expected, demonstrating that concerns about the influence of celebrity drug use and its impact on future health risk behaviour among young people may have been over-simplified and exaggerated. This study illustrates the need to understand young people and their frames of reference within popular culture when designing drug awareness information relevant to them. Furthermore, it indicates that critical media skills analysis may contribute to health risk education programmes related to drug use.

Critical practice and the public pedagogy of environmental and conservation media

This article addresses the reluctance of mainstream corporate and commercial media to critically address major environmental and conservation issues. The resulting public pedagogy largely reproduces the neoliberal ideology informing much conservation practice and discourse. Nonetheless, the media retains an unrealised critical educative potential that needs to be drawn upon by critical media practitioners and educators. To do this, educators need to be cognisant of the phenomenological experience of spectatorship, the aesthetic form and relational contexts of media consumption, production and informal learning. Referring to the work of Vivian Sobchack, Henry Giroux, Pierre Bourdieu and Gilles Deleuze, the article argues that if critical practitioner-educators apply an analytic framework informed by critical realism, counter-hegemonic elements found within corporate and independent media productions and conservation initiatives may be rearticulated and re-presented in a more positive manner. For this to occur, critical media practitioners-educators need to recognise that feasible political and normative alternatives are both available and practically possible. The article ends by discussing some relatively recent non-fiction productions that express a commonality between human and non-human animals and so form the basis of a critical environmental education-media practice.

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

Making sense of microposts (#MSM2013) concept extraction challenge

Microposts are small fragments of social media content that have been published using a lightweight paradigm (e.g. Tweets, Facebook likes, foursquare check-ins). Microposts have been used for a variety of applications (e.g., sentiment analysis, opinion mining, trend analysis), by gleaning useful information, often using third-party concept extraction tools. There has been very large uptake of such tools in the last few years, along with the creation and adoption of new methods for concept extraction. However, the evaluation of such efforts has been largely consigned to document corpora (e.g. news articles), questioning the suitability of concept extraction tools and methods for Micropost data. This report describes the Making Sense of Microposts Workshop (#MSM2013) Concept Extraction Challenge, hosted in conjunction with the 2013 World Wide Web conference (WWW'13). The Challenge dataset comprised a manually annotated training corpus of Microposts and an unlabelled test corpus. Participants were set the task of engineering a concept extraction system for a defined set of concepts. Out of a total of 22 complete submissions 13 were accepted for presentation at the workshop; the submissions covered methods ranging from sequence mining algorithms for attribute extraction to part-of-speech tagging for Micropost cleaning and rule-based and discriminative models for token classification. In this report we describe the evaluation process and explain the performance of different approaches in different contexts.