What is, and what might be, learned from images shared during Twitter conversations among professionals?

This thesis explores the pedagogical potential of images shared during intra-professional conversations held on the social media platform, Twitter. Twitter chats are loosely synchronous exchanges of tweets sharing a unique, identifying keyword or hashtag. They are increasingly being used among professionals to create professional networks in which practice-knowledge and opinion might be shared and where communal connections may be created. As such, they may serve as sites in which professional learning unfolds, both in relation to workplace practices and in relation to the development of new forms of professional practice around social media use. Because the exchanges and broadcasts on Twitter are, for the most part, public, and the conversations are ongoing, they also provide open, freely-accessible, and constantly renewing resources for use in pre-service learning contexts. The research focused on two example chats, one held among midwives and the other among teachers. Inspired by the increasing use of images in new forms of digital communication, the research used images tweeted during the chats as starting points from which to explore flows of knowledge and affect. Data were generated from observations of the two Twitter chats over extended periods, together with interviews with practising professionals, student professionals and their educators in which images were used as elicitation devices. The research combined an approach to reading and “being with” data inspired by ideas drawn from the work of Deleuze (1994; Williams 2013) and Deleuze and Guattari (1988; Massumi 1992), with approaches to reading images drawn from visual social semiotics (Kress and van Leeuwen 1996). The findings suggest that Twitter chats such as those studied here can provide rich opportunities for professional learning. Practice knowledge can flow from one participant to many others, and flows of affect can be used to remoralize individuals and communities. Both chats seemed to serve as sites in which professionals could experience a positivity and affirmation that was not always available in the workplace. However, the forces and intensities at play in these spaces influence both what is said and what is not said, creating new norms of online interaction that generally seemed to avoid negative comments or open disagreement. Educators saw potential to use images such as those shared in the chats in a variety of ways. For example, images could be used as prompts for examination and critique of practices. The educators I interviewed also suggested that the images could be used to help student professionals develop their sensitivity to the forces and intensities that produce particular practices. Group interviews with student professionals suggested that the former happened spontaneously when students encountered and discussed such images, but that the latter might need deliberate facilitation or prompting. The thesis concludes with some recommendations for: (i) educators considering using such images in pre-service professional learning; (ii) professional developers considering using Twitter chats; and (iii) policy-makers involved in drafting guidelines for professionals’ use of social media

Massive Open Online Courses as affinity spaces for connected learning: Exploring effective learning interactions in one massive online community

This paper describes a participatory online culture – Connected Learning Massive Open Online Collaboration (CLMOOC) – and asks how its ethos of reciprocity and creative playfulness occurs. By analysing Twitter interactions over a four-week period, we conclude that this is due to the supportive nature of participants, who describe themselves as belonging to, or connected with, the community. We suggest that Gee’s concept of an affinity space is an appropriate model for CLMOOC and ask how this might be replicated in a higher education setting

Crowd-Sourced Focus Groups on Twitter: 140 Characters of Research Insight

Researchers have traditionally relied on in-person focus groups to test and obtain feedback regarding behavioral and technology-based interventions for specific disease processes. An increasing generation of engaged and connected patients turn to Twitter, a popular microblogging service, to discuss health related topics. Regularly scheduled Twitter-based chats (tweetchats) can potentially function as an additional source of input and information from a diverse, global group of engaged participants. We report the first use of a “tweetchat focus group” to explore data collection issues using this methodology. The speed at which tweetchat conversations occur, coupled with the ability to pursue multiple streams of conversation both in real time and in a delayed fashion, make tweetchat data collection particularly challenging. We discuss important considerations and preparation that should be undertaken by the researchers prior to initiating a tweetchat focus group, consider facilitation challenges and issues of confidentiality.

Investigation of the utilisation of social networks in e-learning at universities

Over the years universities have considered to use social networks for learning purposes as most of their students now engage on them. However, questions on the impact social networks would have on learning and how they can be utilised further for more effective teaching and learning are still unclear. To solve these questions, an in-depth investigation has been conducted to understand the benefits and drawback of social network features available for students. The investigation results show that students strongly believe that social network features will help enhance learning and the key ways of utilising such features have been suggested

Characterizing Attention Cascades in WhatsApp Groups

An important political and social phenomena discussed in several countries, like India and Brazil, is the use of WhatsApp to spread false or misleading content. However, little is known about the information dissemination process in WhatsApp groups. Attention affects the dissemination of information in WhatsApp groups, determining what topics or subjects are more attractive to participants of a group. In this paper, we characterize and analyze how attention propagates among the participants of a WhatsApp group. An attention cascade begins when a user asserts a topic in a message to the group, which could include written text, photos, or links to articles online. Others then propagate the information by responding to it. We analyzed attention cascades in more than 1.7 million messages posted in 120 groups over one year. Our analysis focused on the structural and temporal evolution of attention cascades as well as on the behavior of users that participate in them. We found specific characteristics in cascades associated with groups that discuss political subjects and false information. For instance, we observe that cascades with false information tend to be deeper, reach more users, and last longer in political groups than in non-political groups.Comment: Accepted as a full paper at the 11th International ACM Web Science Conference (WebSci 2019). Please cite the WebSci versio

Encounters on the social web: Everyday life and emotions online

Encounters also happen online nowadays and, yes, they are still difficult to describe, even though it is sometimes easier to observe them-and obtain data about them- than in the past. The internet is crucially 'shaping the interactions people have with one another' (Johns 2010: 499). With the recent explosion and popularity of Web 2.0 services and the social web, such as Facebook (FB), Twitter, and various other types of social media, internet users now have at their disposal an unprecedented collection of tools to interact with others. These modes of online sociability allow users to pursue social encounters with variable levels of involvement, attention, and activity (Papacharissi and Mendelson 2010). For many of us it is now difficult to imagine our social relationships without access to the internet. The social web plays an important role in relationships among internet users (Boyd 2006), with the expression, management and experience of emotions being key to the maintenance of these relationships

"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201

Teens & Online Video

Presents survey findings about teens' likelihood of recording and uploading video, streaming video live, and using video chat applications by gender, age, race/ethnicity, household income, parents' education, and community type