Domestic violence on #qanda: The “Man” question in live Twitter discussion on the Australian Broadcasting Corporation's Q&A

Domestic violence is currently undergoing a period of heightened visibility in Australia. This article uses social media to analyze public discussions about this violence with respect to a specific theoretical frame, which Adrian Howe has called the “Man” question: where and how are men visible or invisible in narratives about their violence against women? The article presents a qualitative study of the Twitter conversation surrounding a special episode of the Australian Broadcasting Corporation's television program Q&A, themed around family violence, which aired in February 2015. We found that the place of men in this conversation was contested. Some tweets privileged men's voices and concerns, as did the organization and production of the program. However, feminist voices were also highly visible via presenting facts, legitimating survivor voices, and recuperating anti-feminist memes to challenge hegemonic patriarchal discourses on men's violence against women. La violence conjugale connait actuellement une visibilité accrue en Australie. Les auteures du présent article utilisent les réseaux sociaux pour analyser les débats publics sur cette violence selon un cadre théorique précis, qu'Adrian Howe a appelé la question de « l'homme » : où et comment les hommes sont-ils visibles ou invisibles dans les récits de leur violence envers les femmes? L'article présente une étude qualitative d'une conversation sur Twitter au sujet d'un épisode axé sur la famille diffusé en février 2015 dans le cadre de l'émission Q & A, à la télévision nationale d'Australie. Nous avons remarqué que dans cette conversation la place des hommes était remise en question. Certains tweets privilégiaient les voix et les craintes des hommes, comme l'ont fait les organisateurs et les producteurs de l'émission. Cependant, il y avait une forte présence de voix féministes dans la présentation des faits, légitimant le point de vue des survivantes et relevant des éléments culturels antiféministes afin de défier les discours hégémoniques et patriarcaux sur la violence des hommes envers les femmes.

Análisis y modelización de la dinámica emergente durante el proceso de difusión de información en las redes sociales de internet

Durante la actividad diaria, la sociedad actual interactúa constantemente por medio de dispositivos electrónicos y servicios de telecomunicaciones, tales como el teléfono, correo electrónico, transacciones bancarias o redes sociales de Internet. Sin saberlo, masivamente dejamos rastros de nuestra actividad en las bases de datos de empresas proveedoras de servicios. Estas nuevas fuentes de datos tienen las dimensiones necesarias para que se puedan observar patrones de comportamiento humano a grandes escalas. Como resultado, ha surgido una reciente explosión sin precedentes de estudios de sistemas sociales, dirigidos por el análisis de datos y procesos computacionales. En esta tesis desarrollamos métodos computacionales y matemáticos para analizar sistemas sociales por medio del estudio combinado de datos derivados de la actividad humana y la teoría de redes complejas. Nuestro objetivo es caracterizar y entender los sistemas emergentes de interacciones sociales en los nuevos espacios tecnológicos, tales como la red social Twitter y la telefonía móvil. Analizamos los sistemas por medio de la construcción de redes complejas y series temporales, estudiando su estructura, funcionamiento y evolución en el tiempo. También, investigamos la naturaleza de los patrones observados por medio de los mecanismos que rigen las interacciones entre individuos, así como medimos el impacto de eventos críticos en el comportamiento del sistema. Para ello, hemos propuesto modelos que explican las estructuras globales y la dinámica emergente con que fluye la información en el sistema. Para los estudios de la red social Twitter, hemos basado nuestros análisis en conversaciones puntuales, tales como protestas políticas, grandes acontecimientos o procesos electorales. A partir de los mensajes de las conversaciones, identificamos a los usuarios que participan y construimos redes de interacciones entre los mismos. Específicamente, construimos una red para representar quién recibe los mensajes de quién y otra red para representar quién propaga los mensajes de quién. En general, hemos encontrado que estas estructuras tienen propiedades complejas, tales como crecimiento explosivo y distribuciones de grado libres de escala. En base a la topología de estas redes, hemos indentificado tres tipos de usuarios que determinan el flujo de información según su actividad e influencia. Para medir la influencia de los usuarios en las conversaciones, hemos introducido una nueva medida llamada eficiencia de usuario. La eficiencia se define como el número de retransmisiones obtenidas por mensaje enviado, y mide los efectos que tienen los esfuerzos individuales sobre la reacción colectiva. Hemos observado que la distribución de esta propiedad es ubicua en varias conversaciones de Twitter, sin importar sus dimensiones ni contextos. Con lo cual, sugerimos que existe universalidad en la relación entre esfuerzos individuales y reacciones colectivas en Twitter. Para explicar los factores que determinan la emergencia de la distribución de eficiencia, hemos desarrollado un modelo computacional que simula la propagación de mensajes en la red social de Twitter, basado en el mecanismo de cascadas independientes. Este modelo nos permite medir el efecto que tienen sobre la distribución de eficiencia, tanto la topología de la red social subyacente, como la forma en que los usuarios envían mensajes. Los resultados indican que la emergencia de un grupo selecto de usuarios altamente eficientes depende de la heterogeneidad de la red subyacente y no del comportamiento individual. Por otro lado, hemos desarrollado técnicas para inferir el grado de polarización política en redes sociales. Proponemos una metodología para estimar opiniones en redes sociales y medir el grado de polarización en las opiniones obtenidas. Hemos diseñado un modelo donde estudiamos el efecto que tiene la opinión de un pequeño grupo de usuarios influyentes, llamado élite, sobre las opiniones de la mayoría de usuarios. El modelo da como resultado una distribución de opiniones sobre la cual medimos el grado de polarización. Aplicamos nuestra metodología para medir la polarización en redes de difusión de mensajes, durante una conversación en Twitter de una sociedad políticamente polarizada. Los resultados obtenidos presentan una alta correspondencia con los datos offline. Con este estudio, hemos demostrado que la metodología propuesta es capaz de determinar diferentes grados de polarización dependiendo de la estructura de la red. Finalmente, hemos estudiado el comportamiento humano a partir de datos de telefonía móvil. Por una parte, hemos caracterizado el impacto que tienen desastres naturales, como innundaciones, sobre el comportamiento colectivo. Encontramos que los patrones de comunicación se alteran de forma abrupta en las áreas afectadas por la catástofre. Con lo cual, demostramos que se podría medir el impacto en la región casi en tiempo real y sin necesidad de desplegar esfuerzos en el terreno. Por otra parte, hemos estudiado los patrones de actividad y movilidad humana para caracterizar las interacciones entre regiones de un país en desarrollo. Encontramos que las redes de llamadas y trayectorias humanas tienen estructuras de comunidades asociadas a regiones y centros urbanos. En resumen, hemos mostrado que es posible entender procesos sociales complejos por medio del análisis de datos de actividad humana y la teoría de redes complejas. A lo largo de la tesis, hemos comprobado que fenómenos sociales como la influencia, polarización política o reacción a eventos críticos quedan reflejados en los patrones estructurales y dinámicos que presentan la redes construidas a partir de datos de conversaciones en redes sociales de Internet o telefonía móvil. ABSTRACT During daily routines, we are constantly interacting with electronic devices and telecommunication services. Unconsciously, we are massively leaving traces of our activity in the service providers’ databases. These new data sources have the dimensions required to enable the observation of human behavioral patterns at large scales. As a result, there has been an unprecedented explosion of data-driven social research. In this thesis, we develop computational and mathematical methods to analyze social systems by means of the combined study of human activity data and the theory of complex networks. Our goal is to characterize and understand the emergent systems from human interactions on the new technological spaces, such as the online social network Twitter and mobile phones. We analyze systems by means of the construction of complex networks and temporal series, studying their structure, functioning and temporal evolution. We also investigate on the nature of the observed patterns, by means of the mechanisms that rule the interactions among individuals, as well as on the impact of critical events on the system’s behavior. For this purpose, we have proposed models that explain the global structures and the emergent dynamics of information flow in the system. In the studies of the online social network Twitter, we have based our analysis on specific conversations, such as political protests, important announcements and electoral processes. From the messages related to the conversations, we identify the participant users and build networks of interactions with them. We specifically build one network to represent whoreceives- whose-messages and another to represent who-propagates-whose-messages. In general, we have found that these structures have complex properties, such as explosive growth and scale-free degree distributions. Based on the topological properties of these networks, we have identified three types of user behavior that determine the information flow dynamics due to their influence. In order to measure the users’ influence on the conversations, we have introduced a new measure called user efficiency. It is defined as the number of retransmissions obtained by message posted, and it measures the effects of the individual activity on the collective reacixtions. We have observed that the probability distribution of this property is ubiquitous across several Twitter conversation, regardlessly of their dimension or social context. Therefore, we suggest that there is a universal behavior in the relationship between individual efforts and collective reactions on Twitter. In order to explain the different factors that determine the user efficiency distribution, we have developed a computational model to simulate the diffusion of messages on Twitter, based on the mechanism of independent cascades. This model, allows us to measure the impact on the emergent efficiency distribution of the underlying network topology, as well as the way that users post messages. The results indicate that the emergence of an exclusive group of highly efficient users depends upon the heterogeneity of the underlying network instead of the individual behavior. Moreover, we have also developed techniques to infer the degree of polarization in social networks. We propose a methodology to estimate opinions in social networks and to measure the degree of polarization in the obtained opinions. We have designed a model to study the effects of the opinions of a small group of influential users, called elite, on the opinions of the majority of users. The model results in an opinions distribution to which we measure the degree of polarization. We apply our methodology to measure the polarization on graphs from the messages diffusion process, during a conversation on Twitter from a polarized society. The results are in very good agreement with offline and contextual data. With this study, we have shown that our methodology is capable of detecting several degrees of polarization depending on the structure of the networks. Finally, we have also inferred the human behavior from mobile phones’ data. On the one hand, we have characterized the impact of natural disasters, like flooding, on the collective behavior. We found that the communication patterns are abruptly altered in the areas affected by the catastrophe. Therefore, we demonstrate that we could measure the impact of the disaster on the region, almost in real-time and without needing to deploy further efforts. On the other hand, we have studied human activity and mobility patterns in order to characterize regional interactions on a developing country. We found that the calls and trajectories networks present community structure associated to regional and urban areas. In summary, we have shown that it is possible to understand complex social processes by means of analyzing human activity data and the theory of complex networks. Along the thesis, we have demonstrated that social phenomena, like influence, polarization and reaction to critical events, are reflected in the structural and dynamical patterns of the networks constructed from data regarding conversations on online social networks and mobile phones.

How long is a tweet? Mapping dynamic conversation networks on Twitter using Gawk and Gephi

Twitter is now well established as the world’s second most important social media platform, after Facebook. Its 140-character updates are designed for brief messaging, and its network structures are kept relatively flat and simple: messages from users are either public and visible to all (even to unregistered visitors using the Twitter website), or private and visible only to approved ‘followers’ of the sender; there are no more complex definitions of degrees of connection (family, friends, friends of friends) as they are available in other social networks. Over time, Twitter users have developed simple, but effective mechanisms for working around these limitations: ‘#hashtags’, which enable the manual or automatic collation of all tweets containing the same #hashtag, as well allowing users to subscribe to content feeds that contain only those tweets which feature specific #hashtags; and ‘@replies’, which allow senders to direct public messages even to users whom they do not already follow. This paper documents a methodology for extracting public Twitter activity data around specific #hashtags, and for processing these data in order to analyse and visualize the @reply networks existing between participating users – both overall, as a static network, and over time, to highlight the dynamic structure of @reply conversations. Such visualizations enable us to highlight the shifting roles played by individual participants, as well as the response of the overall #hashtag community to new stimuli – such as the entry of new participants or the availability of new information. Over longer timeframes, it is also possible to identify different phases in the overall discussion, or the formation of distinct clusters of preferentially interacting participants.

(Not) the Twitter election: The dynamics of the #ausvotes conversation in relation to the Australian media ecology

This paper draws on a larger study of the uses of Australian user-created content and online social networks to examine the relationships between professional journalists and highly engaged Australian users of political media within the wider media ecology, with a particular focus on Twitter. It uses an analysis of topic based conversation networks using the #ausvotes hashtag on Twitter around the 2010 federal election to explore the key themes and issues addressed by this Twitter community during the campaign, and finds that Twitter users were largely commenting on the performance of mainstream media and politicians rather than engaging in direct political discussion. The often critical attitude of Twitter users towards the political establishment mirrors the approach of news and political bloggers to political actors, nearly a decade earlier, but the increasing adoption of Twitter as a communication tool by politicians, journalists, and everyday users alike makes a repetition of the polarisation experienced at that time appear unlikely.

The conversation on political issues on Twitter: an analysis of the participation and frames in the debate on the 'Wert law' and evictions in Spain

The aim of this study is to determine which social agents are involved in the political debate on Twitter and whether the interpretive hegemony of actors that have traditionally been the most prominent is tempered by the challenge of framing shared with audiences. The relationship between the interpretations expressed and the profiles of participants is analyzed in comparison with the frames used by mainstream media. The chosen methodology combines content analysis and discourse analysis techniques on a sample of 1,504 relevant tweets posted on two political issues –the approval of the education law LOMCE and the evictions caused by the crisis, which have also been studied in the front pages of four leading newspapers in Spain. The results show a correlation between political issue singularities, frames and the type of discussion depending on the participants.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

For more than a decade research in the field of context aware computing has aimed to find ways to exploit situational information that can be detected by mobile computing and sensor technologies. The goal is to provide people with new and improved applications, enhanced functionality and better use experience (Dey, 2001). Early applications focused on representing or computing on physical parameters, such as showing your location and the location of people or things around you. Such applications might show where the next bus is, which of your friends is in the vicinity and so on. With the advent of social networking software and microblogging sites such as Facebook and Twitter, recommender systems and so on context-aware computing is moving towards mining the social web in order to provide better representations and understanding of context, including social context. In this paper we begin by recapping different theoretical framings of context. We then discuss the problem of context- aware computing from a design perspective.

Sharing the news : Dissemination of links to Australian news sites on Twitter

Social media adoption in Australia, which provides the geographic focus for this chapter, has been rapid and substantial (ABC News, 2010) – possibly because of the considerable dispersal of the Australian population across the continent, as well as the significant distance of the country from many of its closest partner nations. Social media can play an important role in strengthening and maintaining interpersonal and professional relationships in spite of such physical distance; in particular, social media services are now well-recognised as important tools for the dissemination of news across many developed nations. Hermida (2010) and Burns (2010) both speak of Twitter as a medium for “ambient news”, for example: always-on, operating as a steady stream in the background and at the edge of users’ conscious perception. Much as ambient music is designed to do, it comes to the fore when notable events (such as major breaking news) lead to an increase in volume and demand a greater level of attention from users.

#ausvotes Mark Two : Twitter in the 2013 Australian Federal Election

In this paper, we explore the use of Twitter as a political tool in the 2013 Australian Federal Election. We employ a ‘big data’ approach that combines qualitative and quantitative methods of analysis. By tracking the accounts of politicians and parties, and the tweeting activity to and around these accounts, as well as conversations on particular hashtagged topics, we gain a comprehensive insight into the ways in which Twitter is employed in the campaigning strategies of different parties. We compare and contrast the use of Twitter by political actors with its adoption by citizens as a tool for political conversation and participation. Our study provides an important longitudinal counterpoint, and opportunity for comparison, to the use of Twitter in previous Australian federal and state elections. Furthermore, we offer innovative methodologies for data gathering and evaluation that can contribute to the comparative study of the political uses of Twitter across diverse national media and political systems.

From selfies to climate change : the #G20 debate on Twitter

The G20 Summit that brought many of the world’s most important leaders to Brisbane last weekend was also a major Twitter event. Australian and international users expressed their concerns over the appearance of Russian warships off the Queensland coast, shared selfies from German Chancellor Angela Merkel’s impromptu visit to Brisbane’s Caxton St nightlife hub and called for action on issues ranging from Ebola to climate change...

From News Blogs to News on Twitter: Gatewatching and Collaborative News Curation

This chapter, explores the role of the second tier of independent news blogs as it developed in the years following the Seattle WTO protests in 1999, and outlines the practice of gatewatching as a key element of news bloggers’ activities. We critique perceptions of the news blogosphere as an echo chamber or filter bubble whose discussions about current events are detached from journalistic coverage, and demonstrate instead the close interconnections between independent news bloggers and professional journalists in the wider media ecology. Finally, we sketch the gradual transition and broadening of gatewatching practices in the news blogosphere towards the collaborative curation of news sharing in contemporary social media spaces, and outline the further research questions which emerge from such transformations of the flows of news and discussion.

Identifying relevant information for emergency services from twitter in response to natural disaster

This project proposes a framework that identifies high‐value disaster-based information from social media to facilitate key decision-making processes during natural disasters. At present it is very difficult to differentiate between information that has a high degree of disaster relevance and information that has a low degree of disaster relevance. By digitally harvesting and categorising social media conversation streams automatically, this framework identifies highly disaster-relevant information that can be used by emergency services for intelligence gathering and decision-making.

What's up with Twitter, in fewer than 10,000 characters

The internet erupted in outrage last week at reports that Twitter is poised to increase the limit for tweets from 140 to 10,000 characters. The first rumours of such a move emerged in the tech news website Re/code back in September then again last week. The response on Twitter was immediate and, for the most part, somewhere between incensed and bemused, with many thousands of tweets posted with hashtags such as #10kTwitter, #Twitter10k, #10000gate, #140twitter, #beyond140 and #longtweets...

Twitter as a first draft of the present – and the challenges of preserving it for the future

This paper provides a framework for understanding Twitter as a historical source. We address digital humanities scholars to enable the transfer of concepts from traditional source criticism to new media formats, and to encourage the preservation of Twitter as a cultural artifact. Twitter has established itself as a key social media platform which plays an important role in public, real-time conversation. Twitter is also unique as its content is being archived by a public institution (the Library of Congress). In this paper we will show that we still have to assume that much of the contextual information beyond the pure tweet texts is already lost, and propose additional objectives for preservation.