The history of American architecture includes many examples of activists and reformers who sought to make the profession more inclusive, just, and socially engaged. This article provides a review of the academic literature discussing the efforts of such architects in order to identify historic trends in the study of activist architects in the United States-this paper's focus. After an initial period of growth and consolidation in the profession, contemporary forms of social engagement emerged in the 1960s and 1970s. Subsequent decades have seen many of these efforts continue, or be revived, alongside increased academic interest in these same efforts. The article then reviews three areas of sociological research pertinent to the scholarship on socially engaged design. These focus on institutional change within the profession, the "logics" that guide architectural work, and the relationship between the profession and the academy. This article explores these institutional perspectives for their potential to complement frameworks for analyzing dissent in design.

The goal of this paper is to share a sociological framework for understanding social justice activism with the intention of improving efficacy of architects’ efforts in addressing contentious social issues. The paper draws on recent sociological scholarship on professions and social movements, which give us new ways of thinking about our agency in affecting social change within and beyond the profession. The paper presents emerging themes based on participant observation and unstructured interviews conducted over the past two years, focused on contemporary activism in architecture. We high-light how professionals use their material resources (design expertise and practice) and their symbolic resources (status in socio-economic, political, and cultural systems) in different forms of contentious political engagement. We offer a socio-logical framework for distinguishing between ways architects use their work and status in their efforts to achieve social and professional change. The analysis offered in this paper is intended to offer politically-engaged architects (professionals, educators, and students) a framework to assist in their efforts toward shaping equity and justice outcomes for the field and for society.

This article examines how emerging professionals navigate uncertain conditions in creative fields. Using data from in-depth work history interviews with 55 graphic designers and digital media artists, the findings demonstrate how those doing creative work in commercial settings use boundary work as a narrative strategy that brings order to discordant work experiences. Interviewees engage in two forms of boundary work—segmentation and integration—both of which rely on shared meanings of the value and rewards of creative work. Segmentation refers to rhetorical strategies that combine the competing motivations of work—artistic and commercial—in order to explain combinations of different job types. Integration refers to efforts to merge these motivations, justifying work in a single full-time job that combines artistic and commercial logics. Interviewees in both groups draw on the concept of creativity to evaluate the risks and rewards of work and to justify commercial engagement while bolstering artistic identities. The analysis suggests new directions for sociological research on cultural production, artistic careers, and labor market uncertainty.

Language is in constant flux be it from changes in meaning to the introduction of new terms. At the user level it changes by users accommodating their language in relation to whom they are in contact with. By mining diffusion's of new terms across social networks we detect the influence between users and communities. This is then used to compute the user activation threshold at which they adopt new terms dependent on their neighbours. We apply this method to four different networks from two popular on-line social networks (Reddit and Twitter). This research highlights novel results: by testing the network through random shuffles we show that the time at which a user adopts a term is dependent on the local structure, however, a large part of the influence comes from the global structure and that influence between users and communities is not significantly dependent on network structures.

Existing studies of how information diffuses across social networks have thus far concentrated on analysing and recovering the spread of deterministic innovations such as URLs, hashtags, and group membership. However investigating how mentions of real-world entities appear and spread has yet to be explored, largely due to the computationally intractable nature of performing large-scale entity extraction. In this paper we present, to the best of our knowledge, one of the first pieces of work to closely examine the diffusion of named entities on social media, using Reddit as our case study platform. We first investigate how named entities can be accurately recognised and extracted from discussion posts. We then use these extracted entities to study the patterns of entity cascades and how the probability of a user adopting an entity (i.e. mentioning it) is associated with exposures to the entity. We put these pieces together by presenting a parallelised diffusion model that can forecast the probability of entity adoption, finding that the influence of adoption between users can be characterised by their prior interactions -- as opposed to whether the users propagated entity-adoptions beforehand. Our findings have important implications for researchers studying influence and language, and for community analysts who wish to understand entity-level influence dynamics.

Language change and innovation is constant in online and offline communication, and has led to new words entering people's lexicon and even entering modern day dictionaries, with recent additions of 'e-cig' and 'vape'. However the manual work required to identify these 'innovations' is both time consuming and subjective. In this work we demonstrate how such innovations in language can be identified across two different OSN's (Online Social Networks) through the operationalisation of known language acceptance models that incorporate relatively simple statistical tests. From grounding our work in language theory, we identified three statistical tests that can be applied - variation in; frequency, form and meaning. Each show different success rates across the two networks (Geo-bound Twitter sample and a sample of Reddit). These tests were also applied to different community levels within the two networks allowing for different innovations to be identified across different community structures over the two networks, for instance: identifying regional variation across Twitter, and variation across groupings of Subreddits, where identified example innovations included 'casualidad' and 'cym'.

Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules; each customer has a specific set of constraints (e.g. budget) and application requirements (e.g. minimum computational resources). Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics to determine the most suitable instance type(s) and the most opportune time for starting or migrating instances. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings. We provide an experimental study based on extensive set of job executions over a major public cloud infrastructure.

Churners are users who stop using a given service after previously signing up. In the domain of telecommunications and video games, churners represent a loss of revenue as a user leaving indicates that they will no longer pay for the service. In the context of online community platforms (e.g., community message boards, social networking sites, question--answering systems, etc.), the churning of a user can represent different kinds of loss: of social capital, of expertise, or of a vibrant individual who is a mediator for interaction and communication. Detecting which users are likely to churn from online communities, therefore, enables community managers to offer incentives to entice those users back; as retention is less expensive than re-signing users up. In this article, we tackle the task of detecting churners on four online community platforms by mining user development signals. These signals explain how users have evolved along different dimensions (i.e., social and lexical) relative to their prior behaviour and the community in which they have interacted. We present a linear model, based upon elastic-net regularisation, that uses extracted features from the signals to detect churners. Our evaluation of this model against several state of the art baselines, including our own prior work, empirically demonstrates the superior performance that this approach achieves for several experimental settings. This article presents a novel approach to churn prediction that takes a different route from existing approaches that are based on measuring static social network properties of users (e.g., centrality, in-degree, etc.).

For community managers and hosts it is not only important to identify the current key topics of a community but also to assess the specificity level of the community for: a) creating sub-communities, and: b) anticipating community behaviour and topical evolution. In this paper we present an approach that empirically characterises the topical specificity of online community forums by measuring the abstraction of semantic concepts discussed within such forums. We present a range of concept abstraction measures that function over concept graphs i.e. resource type-hierarchies and SKOS category structures and demonstrate the efficacy of our method with an empirical evaluation using a ground truth ranking of forums. Our results show that the proposed approach outperforms a random baseline and that resource type-hierarchies work well when predicting the topical specificity of any forum with various abstraction measures.

The emergence and actions of the so-called Islamic State of Iraq and the Levant (ISIL/ISIS) has received widespread news coverage across the World, largely due to their capture of large swathes of land across Syria and Iraq, and the publishing of execution and propaganda videos. Enticed by such material published on social media and attracted to the cause of ISIS, there have been numerous reports of individuals from European countries (the United Kingdom and France in particular) moving to Syria and joining ISIS. In this paper our aim to understand what happens to Europe-based Twitter users before, during, and after they exhibit pro-ISIS behaviour (i.e. using pro-ISIS terms, sharing content from pro-ISIS accounts), characterising such behaviour as radicalisation signals. We adopt a data-mining oriented approach to computationally determine time points of activation (i.e. when users begin to adopt pro-ISIS behaviour), characterise divergent behaviour (both lexically and socially), and quantify influence dynamics as pro-ISIS terms are adopted. Our findings show that: (i) of 154K users examined only 727 exhibited signs of pro-ISIS behaviour and the vast majority of those 727 users became \emph{activated} with such behaviour during the summer of 2014 when ISIS shared many beheading videos online; (ii) users exhibit significant behaviour divergence around the time of their activation, and; (iii) social homophily has a strong bearing on the diffusion process of pro-ISIS terms through Twitter.

From its start, the so-called Islamic State of Iraq and the Levant (ISIL/ISIS) has been successfully exploiting social media networks, most notoriously Twitter, to promote its propaganda and recruit new members, resulting in thousands of social media users adopting pro ISIS stance every year. Automatic identification of pro-ISIS users on social media has, thus, become the centre of interest for various governmental and research organisations. In this paper we propose a semantic-based approach for radicalisation detection on Twitter. Unlike most previous works, which mainly rely on the lexical and contextual representation of the content published by Twitter users, our approach extracts and makes use of the underlying semantics of words exhibited by these users to identify their pro/anti-ISIS stances. Our results show that classifiers trained from words’ semantics outperform those trained from lexical and network features by 2% on average F1-measure.

#Microposts2015, the 5th workshop on 'Making Sense of Microposts', is summarised by the sub-theme: 'big things come in small packages'. The workshop was borne out of research we were each carrying out as microblogging platforms became increasingly popular, and their value as a publishing platform and the data generated as a result began to be recognised. This phenomenon continues to grow, as microblogs provide a low-effort means of publishing information within private, but moreso public, fora, giving a voice to all in all arenas.

Language is fundamental to human communication - throughout the course of history language has constantly evolved. This can currently be seen in the changing forms of colloquial language in various on-line social networks (OSN's). These innovations in language are even appearing in every day life with the recent induction of `lol' and `rofl' into modern dictionaries. Changes and varying forms of language pose challenges to both academics and people in business when attempting to assess and communicate with different communities. In this Ph.D, we aim to forecast online language change through the use of predictive and descriptive methodologies. Through using data sets mined from a number of OSNs, we aim to develop generalizable models and theories for assessing and predicting such language changes. We philosophically frame this work by drawing on structuration theory which helps us structure our analysis of the dynamics of language (re)production - i.e. by the agent (user), the social structure and their interplay. We draw on state-of-the-art work and methods, including the development of neural nets to analyse language usage, along with network and community classification too uncover social structures within language. Preliminary results have identified statistically significant innovations usage across communities across a number of OSN's, this was done by operationalizing known linguistic models of innovation acceptance.

Knowing which users are likely to churn (i.e. leave) a service enables service providers to offer retention incentives for users to remain. To date, the prediction of churners has been largely performed through the examination of users’ social network features; in order to see how churners and non-churners differ. In this paper we examine the social and lexical development of churners and non-churners and find that they exhibit visibly different signals over time. We present a prediction model that mines such development signals using Gaussian Sequences in the form of a joint probability model; under the assumption that the values of churners’ and non-churners’ social and lexical signals are normally distributed at a given time point. The evaluation of our approach, and its different permutations, demonstrates that we achieve significantly better performance than state of the art baselines for two of the datasets that we tested the approach on.

Despite their semantic-rich nature, online communities have, to date, largely been analysed through examining longitudinal changes in social networks, community uptake, or simple term-usage and language adoption. As a result, the evolution of communities on a semantic level, i.e. how concepts emerge, and how these concepts relate to previously discussed concepts, has largely been ignored. In this paper we present a graph-based exploration of the semantic evolution of online communities, thereby capturing dynamics of online communities on a conceptual level. We first examine how semantic graphs (concept graphs and entity graphs) of communities evolve, and then characterise such evolution using logistic population growth models. We demonstrate the value of such models by analysing how sample communities evolve and use our results to predict churn rates in community forums.

Short text messages, a.k.a microposts (e.g., tweets), have proven to be an effective channel for revealing information about trends and events, ranging from those related to disaster (e.g., Hurricane Sandy) to those related to violence (e.g., Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of microposts with features extracted only from the microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures.

Recommender systems profile the preferences of users and then use this information to forecast users' future ratings. One of the most common recommendation approaches is the use of matrix factorisation in which users' past ratings of items (i.e. Movies, books, etc.) are used to capture their affinity to implicit factors. A central limitation of such factorisation is that one cannot consider how a user's preferences for a factor have changed over time. In this paper we present the SemanticSVD++ model that overcomes this limitation by using the semantic categories of recommendation items as prior factors for a given user. We present a model to capture the semantic taste evolution of users over time, and demonstrate how such development is susceptible to global influence dynamics. We explain how the SemanticSVD++ model incorporates such evolution information within a matrix factorisation approach, and empirically demonstrate the improvement in predictive capability that this yields when tested on two independent movie recommendation datasets.