Visual cultures of CRISPR: intermedial figuration in science communication.

Message from the Executive Director: Interview with Britta Voss and Kylla Marie Benes, ASLO 2016 Science Communication Interns

With the advent of social network services such as Twitter, Facebook, Tumbler, and Google+, the research on social network and media analysis has been greatly advanced. In recent years, the interactions among people, sharing of knowledge and experiences, community activities in social network services increase greatly, which would make the research on social networks more important. Furthermore, as social media contents within social network services are rapidly being produced and consumed, the social media contents now account for the majority of content published on the world wide web. Social media is differentiated from traditional media in many aspects such as its frequency, quality, usability, immediacy, and permanence, which leads to significant potential to the social media analysis research. The ACM SAC has been an important venue for the past 31 years, attracting computer scientists, computer engineers, software engineers, and application developers from around the world. The Social Network and Media Analysis (SONAMA) track of ACM SAC will provide a forum that brings together researchers and practitioners for exploring technologies, issues, experiences, and applications with a specific focus on the recent research trends and industrial needs in the related fields. Since social network and media analysis encompasses a variety of highly cross-disciplinary research issues, the SONAMA will foster collaborations and exchange of ideas and experiences among researchers working in various fields such as computer science, linguistics, statistics, sociology, geography, economics, and business.

The landscape of contemporary media presents challenges and opportunities for science writers and communicators. These issues have not yet been fully understood. This paper presents the findings of collaborative work conducted to identify the growth in numbers of social media communicators who are writing about science for the Canadian public. We used emerging media research tools, including Altmetrics, and traditional survey tools. Our goal was to help Canada's professional member associations—Science Writers and Communicators of Canada (SWCC) and the Association des Communicateurs Scientifiques du Québec (ACS)— map the changing science communication landscape in Canada. Using an online survey tool, we compared survey responses from social media science communicators we identified to those of professional science communication members of SWCC and the ACS. We found that Canadian social media science communicators were younger, were paid less (or not at all) for their science communication activities, and had been communicating science for fewer years than other science communicators. They were more likely to have a science background (rather than communication, journalism or education) and were less likely to be members of professional associations. They tended to communicate with one another through their own informal networks. These findings provide professional science communication organizations in Canada with an empirical base from which to develop training, support and outreach activities aimed at improving the quality of public engagement with science in Canada.

The information environment is increasingly composed of online media, especially through the growth and evolution of social media since the early 2000s. This shift is most pronounced for science information in particular, as legacy newspapers cut their science sections and science journalists, communicators, interested publics, and scientists themselves migrated to online-only mediums. Because of these changes, science communicators increasingly rely on social media to engage with peers, stakeholders, and interested publics. The new and changing social media environment, however, also comes with features that can facilitate or limit successful communication. Because bad communication is often worse than no communication, it is important for communicators to understand how the features within and across specific media platforms improve or hinder communication across specific groups, topics, and communication goals. This chapter provides an overview of what we know about practicing successful science communication on social media. It describes why and how science communicators use social media and what the pros and cons of particular features are for communicating on different platforms. It ends with a discussion of how science communication research and training help inform best practices on social media, and how collaborations between researchers and communicators can strengthen science communication.

Overcoming Barriers to Engaging in Science Communication: An Interview with Science Communicator Paige Brown Jarreau

This chapter reviews customer relationship management, social media platforms, and social media analytics, and discusses how social media platforms and social media analytics are used to support social CRM. Social CRM emerged by integrating social media with customer relationship management. Social media offers companies an array of innovative ways to interact with their employees, customers, partners, and other stakeholders. As the user base of social media is growing rapidly, it is crucial for companies to understand their social media platforms, develop a plan to continually integrate social media with CRM, analyze social media data with social media analytics, and quickly respond to the needs of customers. To help CRM managers utilize social media analytics systematically, this chapter discusses various analytics methods and presents analytics processes for social media data.

This chapter reviews customer relationship management, social media platforms, and social media analytics, and discusses how social media platforms and social media analytics are used to support social CRM. Social CRM emerged by integrating social media with customer relationship management. Social media offers companies an array of innovative ways to interact with their employees, customers, partners, and other stakeholders. As the user base of social media is growing rapidly, it is crucial for companies to understand their social media platforms, develop a plan to continually integrate social media with CRM, analyze social media data with social media analytics, and quickly respond to the needs of customers. To help CRM managers utilize social media analytics systematically, this chapter discusses various analytics methods and presents analytics processes for social media data.

Science and Public Engagement in National Parks: Examples and Advice from Young Scientists

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing.

Background: Advances in digital technology have made real-time listening and analysis of traditional and social media discussions possible over a selected geographical area. However, the technology is currently not widely available to inform public health risk communication. Aim: This paper reports the use of digital rumor monitoring system to present major sentiments tracked by Africa Centres for Disease Control and Prevention (Africa CDC) over traditional and social media regarding the COVID-19 vaccine during the first 9 months of the COVID-19 pandemic. Methods: Rumors and misinformation about the COVID-19 vaccine were tracked by Africa CDC over the digital rumor monitoring system from March to November 2020. Traditional media analysis was conducted using African media and human-curated aggregation of open-source content from various African sources. Social media analysis was conducted using geo-located African Twitter and Facebook sources, resulting in a set of content from the media. Results: COVID-19 vaccine had the highest traction among COVID-19 rumors monitored in Africa between March and September 2020. Critical narratives were observed mainly in South Africa, Democratic Republic of Congo, Nigeria, and Kenya, where they undermined public views of the COVID-19 vaccine and the vaccine trials. Analysis shows underlying potential for vaccine acceptance is overshadowed by anti-vaccine rhetoric partly influenced by insufficient information about the vaccine in the public domain and the disapproval of “Western vaccine” by Africans. Conclusion: Realtime digital monitoring of rumors and misinformation about public health issues over social and traditional media is now possible. Health authorities and health institutions need transition to this real-time monitoring and build the capacity of their staff to use information from real-time analysis of rumors and misinformation for designing response. Larger scale investment in the technology is critical to make it available for wider use at the national and sub-national levels in Africa.

With the advent of social network services such as Twitter, Facebook, Tumbler, and Google+, the research on social network and media analysis has been greatly advanced. In recent years, the interactions among people, sharing of knowledge and experiences, community activities in social network services increase greatly, which would make the research on social networks more important. Furthermore, as social media contents within social network services are rapidly being produced and consumed, the social media contents now account for the majority of content published on the world wide web. Social media is differentiated from traditional media in many aspects such as its frequency, quality, usability, immediacy, and permanence, which leads to significant potential to the social media analysis research.

It is our great pleasure to welcome you to the SoMeRA 2014: International Workshop on Social Media Retrieval and Analysis, co-located with SIGIR 2014 in Gold Coast, Australia. The amount of user-generated data (including content and contextual information of the users) has been spiraling during the past few years. Social media are fundamentally changing the way how we communicate. Nowadays, people create, share, and consume a huge number of multimedia material on the web and in particular on social platforms. The faster the growth of these corpora, the harder it gets for the individual to find the media documents which satisfy a particular information need. When it comes to multimedia material in particular, the users might also exhibit an entertainment need, which may involve aspects of novelty, serendipity, familiarity, or popularity. However, current retrieval, recommendation, and browsing techniques often fall short to deal with user-generated data of various kinds (audio, image, video, text, contextual, etc.), especially on a larger scale. Satisfying the information- or entertainment need of users in social media data requires a comprehensive understanding of them, which can be gained to some extent by means of social media analysis and -mining. Corresponding user models which are built from this knowledge will improve retrieval and recommendation in social media, going far beyond text-based search which is still the most common paradigm. The gained knowledge also enables intelligently informed and enriched applications in various media domains. The purpose of SoMeRA 2014 is to bring together researchers of different domains who are involved in social media analysis, mining, and retrieval, for instance, experts in multimedia, recommender systems, and user modeling. This is reflected by the 19 submissions received that cover topics as diverse as multimedia retrieval and exploration, user-aware recommender systems, network analysis, event detection, and computational linguistics in social media. Out of these, we selected the most outstanding works to be presented at the workshop, which features 5 oral and 8 poster presentations. In addition, the program includes a keynote speech by Prof. Tat-Seng Chua, National University of Singapore, entitled "From Social Media Data to Actionable Analytics".

Today, multimedia data are produced in massive quantities, thanks to a diverse spectrum of applications including entertainment, surveillance, e-commerce, web, and social media. In particular, social media data have three challenging characteristics: data sizes are enormous, data are often multi-faceted, and data are dynamic. Tensors (multi-dimensional arrays) are widely used for representing such high-order dimensional data. Consequently, a system dealing with social media data needs to scale with the tensor volume and the number and diversity of the data facets. This necessitates highly parallelizable, and in many cases cloud-based, frameworks for scalable processing and efficient analysis of large media and social media collections.Most multimedia applications share a few core operations, including integration/fusion, classification, clustering, graph analysis, near-neighbor search, and similarity search. When performed naively, however, these core operations are often very costly, because the number of objects and object features that need to be considered can be prohibitive. Avoiding this cost requires that redundant work is avoided. Thus, for the next generation cloud-based massive media processing and analysis systems to have transformative impact, the fundamental principles that govern their design must include an awareness of the utilities of data and features to a particular analysis task.Recently, the observation that - while not all - a significant class of data processing applications can be expressed in terms of a small set of primitives that are, in many cases, easy to parallelize, has led to frameworks, such as MapReduce, which have been successfully applied in data processing, mining, and information retrieval domains. Yet, in many other domains (including many aggregation and join tasks that are hard to parallelize) they significantly lag behind traditional solutions. In particular, many multimedia and social media analysis tasks are in the category of applications that pose significant challenges.In this talk, I will present an overview of recent developments in the area of scalable multimedia and social media retrieval and analysis in the cloud and our own efforts [1, 2, 3, 4, 5, 6] to build a scalable data processing middleware, called RanKloud, specifically sensitive to the needs and requirements of multimedia and social media analysis applications. RanKloud avoids waste by intelligently partitioning the data and allocating it on available resources to minimize the data replication and indexing overheads and to prune superfluous low-utility processing. It also includes a tensor-based relational data model to support the complete lifecycle (from collection to analysis) of the data, involving various integration and other manipulation steps. RanKloud also addresses the computational cost of various multi-dimensional data analysis operations, including decomposition or structural change detection, by (a) leveraging a priori background knowledge (or metadata) about one or more domain dimensions and (b) by extending compressed sensing (CS) to tensor data to encode the observed tensor streams in the form of compact descriptors.RanKloud will extend the scope of cloud-based systems to the delivery of efficient and large scale analysis over data with variable utility and, thus, will enable new and efficient applications, tools, and systems for multimedia and social media retrieval and analysis.

The revolutionization of information and communication through new media not only developed diverse needs but also blessed people to gratify those needs from single platform. The fundamental needs i-e information, communication, socialization, escapism as per Uses and Gratification theory is being gratified by social media. Uses and gratification theory conceptualize active role of audience and abets in the evaluation of how audiences use a particular medium and the gratifications they derive from that use. The replacement of traditional media by social media compelled media researchers to do in-depth analysis of social media. This current study aims at exploring difference between traditional and social media news and analyzed their role in gratification of informational need on the basis of gender and usage. Five categories of local, national, international, disaster and entertainment are selected for the survey which consists of 1383 male and female university student from capital of each province of Pakistan. The results are statistically analyzed by using SPSS. The findings suggest that social media remain active all the time to satiate informational need and dependency of traditional media on social media posit that soon traditional media would be displaced. The obtained gratification and individual perception of participants is found significant for social media than traditional media. The moderation effect of gender and usage is also momentous. The male dominance is not only prevalent in social structure but also evident in media consumption patterns. The social and gender disparities are reflective in this current study in informational need gratification through media.

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks.