I have to want to do it : gaining 'voluntary' compliance with fear, guilt, and shame appeals in social marketing campaigns

This paper presents results from a qualitative study of income support recipients with regard to how they feel about advertising which overtly appeals to their sense of fear, guilt and shame. The motivation of the study was to provide formative research for a social marketing campaign designed to increase compliance with income reporting requirements. This study shows that negative appeals with this group of people are more likely to invoke self-protection and inaction rather than an active response such as volunteering to comply. Social marketers need to consider the use fear, guilt and shame to gain voluntary compliance as the study suggests that there has been an overuse of these negative appeals. While more formative research is required, the future research direction aim would be to develop an instrument to measure the impact of shame on prosocial decision-making; particularly in the context of social networks rather than the wider society.

Men in social work : challenging or reproducing an unequal gender regime?

This article is concerned with the reproduction of gender inequality in social work and the extent to which the presence of men in the profession challenges discriminatory processes and occupational segregation. Although it is argued that men need to take more responsibility for caring roles in professions like social work, many of the rationales for encouraging more men to enter social work are unlikely to support alternative masculinities that will challenge gender inequalities. Only a profeminist commitment informing antisexist practices will enable men to address gender inequality in social work.

Emotional reactions to real-world events in social networks

A convergence of emotions among people in social networks is potentially resulted by the occurrence of an unprecedented event in real world. E.g., a majority of bloggers would react angrily at the September 11 terrorist attacks. Based on this observation, we introduce a sentiment index, computed from the current mood tags in a collection of blog posts utilizing an affective lexicon, potentially revealing subtle events discussed in the blogosphere. We then develop a method for extracting events based on this index and its distribution. Our second contribution is establishment of a new bursty structure in text streams termed a sentiment burst. We employ a stochastic model to detect bursty periods of moods and the events associated. Our results on a dataset of more than 12 million mood-tagged blog posts over a 4-year period have shown that our sentiment-based bursty events are indeed meaningful, in several ways.

Building genetic networks using relatedness information : a novel approach for the estimation of dispersal and characterization of group structure in social animals

Natal dispersal is an important life history trait driving variation in individual fitness, and therefore, a proper understanding of the factors underlying dispersal behaviour is critical to many fields including population dynamics, behavioural ecology and conservation biology. However, individual dispersal patterns remain difficult to quantify despite many years of research using direct and indirect methods. Here, we quantify dispersal in a single intensively studied population of the cooperatively breeding chestnut-crowned babbler (Pomatostomus ruficeps) using genetic networks created from the combination of pairwise relatedness data and social networking methods and compare this to dispersal estimates from re-sighting data. This novel approach not only identifies movements between social groups within our study sites but also provides an estimation of immigration rates of individuals originating outside the study site. Both genetic and re-sighting data indicated that dispersal was strongly female biased, but the magnitude of dispersal estimates was much greater using genetic data. This suggests that many previous studies relying on mark–recapture data may have significantly underestimated dispersal. An analysis of spatial genetic structure within the sampled population also supports the idea that females are more dispersive, with females having no structure beyond the bounds of their own social group, while male genetic structure expands for 750 m from their social group. Although the genetic network approach we have used is an excellent tool for visualizing the social and genetic microstructure of social animals and identifying dispersers, our results also indicate the importance of applying them in parallel with behavioural and life history data.

A multidimensional framework for embedded academic skill development : transition pedagogy in social work

As a consequence of the widening participation agenda, student cohorts in Australian higher education are becoming increasingly diverse. While diversity is often characterised by a focus on culture or ethnicity, this variability also independently exists in regard to competence in academic skills (Dillon, 2007). Successfully developing discipline-specific academic skills is crucial to a student’s learning, progress and attainment in higher education. The growing recognition that students are entering Australian universities with varying levels of academic preparedness as a result of the widening participation agenda has made effective academic skill support even more important, since ‘access without a reasonable chance of success is an empty promise’ (International Associations of Universities, 2008, p. 1).

Event extraction using behaviors of sentiment signals and burst structure in social media

Significant world events often cause the behavioral convergence of the expression of shared sentiment. This paper examines the use of the blogosphere as a framework to study user psychological behaviors, using their sentiment responses as a form of ‘sensor’ to infer real-world events of importance automatically. We formulate a novel temporal sentiment index function using quantitative measure of the valence value of bearing words in blog posts in which the set of affective bearing words is inspired from psychological research in emotion structure. The annual local minimum and maximum of the proposed sentiment signal function are utilized to extract significant events of the year and corresponding blog posts are further analyzed using topic modeling tools to understand their content. The paper then examines the correlation of topics discovered in relation to world news events reported by the mainstream news service provider, Cable News Network, and by using the Google search engine. Next, aiming at understanding sentiment at a finer granularity over time, we propose a stochastic burst detection model, extended from the work of Kleinberg, to work incrementally with stream data. The proposed model is then used to extract sentimental bursts occurring within a specific mood label (for example, a burst of observing ‘shocked’). The blog posts at those time indices are analyzed to extract topics, and these are compared to real-world news events. Our comprehensive set of experiments conducted on a large-scale set of 12 million posts from Livejournal shows that the proposed sentiment index function coincides well with significant world events while bursts in sentiment allow us to locate finer-grain external world events.

De-tooling technology : networked computing as an environment, purpose and medium for social action

Network technologies are very desirable for social action, allowing activists to achieve more with less, more quickly and with broader impact; on the other hand, the very advantages they bring are equally important to the world of contemporary capitalism that social action seeks to change. Thus, we must look beyond network technologies as the easy solution to every problem, and focus instead on the human relationships which might be enabled by them. This focus on relationships requires us to ‘de-tool’ information technology. Instead, for social action, it is more valuable to think of networked computing as part of the environment within which action can occur; an important purpose for such action; and as a medium that nurtures expression and engagement of self and belief.

Embedded restricted Boltzmann machines for fusion of mixed data types and applications in social measurements analysis

Analysis and fusion of social measurements is important to understand what shapes the public’s opinion and the sustainability of the global development. However, modeling data collected from social responses is challenging as the data is typically complex and heterogeneous, which might take the form of stated facts, subjective assessment, choices, preferences or any combination thereof. Model-wise, these responses are a mixture of data types including binary, categorical, multicategorical, continuous, ordinal, count and rank data. The challenge is therefore to effectively handle mixed data in the a unified fusion framework in order to perform inference and analysis. To that end, this paper introduces eRBM (Embedded Restricted Boltzmann Machine) – a probabilistic latent variable model that can represent mixed data using a layer of hidden variables transparent across different types of data. The proposed model can comfortably support largescale data analysis tasks, including distribution modelling, data completion, prediction and visualisation. We demonstrate these versatile features on several moderate and large-scale publicly available social survey datasets.

A sentiment-aware approach to community formation in social media

Participating in a community exemplifies the aspect of sharing, networking and interacting in a social media system. There has been extensive work on characterising on-line communities by their contents and tags using topic modelling tools. However, the role of sentiment and mood has not been studied. Arguably, mood is an integral feature of a text, and becomes more significant in the context of social media: two communities might discuss precisely the same topics, yet within an entirely different atmosphere. Such sentiment-related distinctions are important for many kinds of analysis and applications, such as community recommendation. We present a novel approach to identification of latent hyper-groups in social communities based on users’ sentiment. The results show that a sentiment-based approach can yield useful insights into community formation and metacommunities, having potential applications in, for example, mental health—by targeting support or surveillance to communities with negative mood—or in marketing—by targeting customer communities having the same sentiment on similar topics.

Teaching critical reflection: a tool for transformative learning in social work?

In an Australian Bachelor of Social Work degree, critical reflection is a process explicitly taught in a fourth year subject to students who have returned from their first field placement experience in agencies delivering social work programmes. The purpose of teaching critical reflection is to enable social work students to become autonomous and critical thinkers who can reflect on society, the role of social work and social work practices. The way critical reflection is taught in this fourth year social work unit relates closely to the aims of transformative learning. Transformative learning aims to assist students to become autonomous thinkers. Specifically, the critical reflection process taught in this subject aims to assist students to recognise their own and other people's frames of reference, to identify the dominant discourses circulating in making sense of their experience, to problematise their taken-for -granted ‘lived experience’, to reconceptualise identity categories, disrupt assumed causal relations and to reflect on how power relations are operating. Critical reflection often draws on many theoretical frameworks to enable the recognition of current modes of thinking and doing. In this paper, we will draw primarily on how post-structural theories, specifically Foucault's theorising, disrupt several taken-for-granted concepts in social work.