Public science communication on Twitter: a visual analytic approach

Interactive Mode of Visual Communication Based on Information Visualization Theory

In the modern environment, visual communication design has become a comprehensive subject that combines technology and art. The development of various technologies has promoted the emergence of new design art forms, and at the same time, it has also promoted the change of design concepts and thinking modes. Visual representation, as a representation practice to express the meaning of information in the form of visual symbols, is a method and means to realize information visualization. Based on visual interface design, this article takes interactivity and user experience as innovation points and digs into the design process of interactive information visualization to achieve the purpose of information transmission. By analyzing the cognitive tasks of users in each stage of visual thinking activities in visual terminals, this article proposes to optimize the interactive design of visual communication from the key points of attention, consciousness, and memory. In order to verify the feasibility of the interactive mode of visual communication based on information visualization theory, this article has carried out several comparative experiments with different models. Experimental results show that this algorithm has a faster convergence speed and a greater generalization ability, the accuracy of the algorithm reaches 96.03%, and the highest evaluation of user satisfaction is 95.93%. The interactive mode of visual communication in this article can provide means and reference value for the development direction of visual communication design.

Going rogue: what scientists can learn about Twitter communication from "alt" government accounts

The inauguration of President Trump in the United States led to the active restriction of science communication from federal agencies, resulting in the creation of many unofficial "alt" Twitter accounts to maintain communication. Alt accounts had many followers (e.g., 15 accounts had > 100,000) and received a large amount of media attention, making them ideal for better understanding how differences in messaging can affect public engagement with science on microblogging platforms. We analyzed tweets produced by alt and corresponding official agency accounts to compare the two groups and determine if specific features of a tweet made them more likely to be retweeted or liked to help the average scientist potentially reach a broader audience on Twitter. We found adding links, images, hashtags, and mentions, as well as expressing angry and annoying sentiments all increased retweets and likes. Evidence-based terms such as "peer-review" had high retweet rates but linking directly to peer-reviewed publications decreased attention compared to popular science websites. Word choice and attention did not reflect official or alt account types, indicating topic is more important than source. The number of tweets generated and attention received by alt accounts has decreased since their creation, demonstrating the importance of timeliness in science communication on social media. Together our results show potential pathways for scientists to increase efficacy in Twitter communications.

Using interpersonal communication strategies to encourage science conversations on social media

Today, many science communicators are using social media to share scientific information with citizens, but, as research has shown, fostering conversational exchanges remains a challenge. This largely qualitative study investigated the communication strategies applied by individual scientists and environmental non-governmental organizations on Twitter and Instagram to determine whether particular social media practices encourage two-way conversations between science communicators and citizens. Data from Twitter and Instagram posts, interviews with the communicators, and a survey of audience members were triangulated to identify emergent communication strategies and the resulting engagement; provide insight into why particular practices are employed by communicators; and explain why audiences choose to participate in social media conversations with communicators. The results demonstrate that the application of interpersonal communication strategies encourage conversational engagement, in terms of the number of comments and unique individuals involved in conversations. In particular, using selfies (images and videos), non-scientific content, first person pronoun-rich captions, and responding to comments result in the formation of communicator-audience relationships, encouraging two-way conversations on social media. Furthermore, the results indicate that Instagram more readily supports the implementation of interpersonal communication strategies than Twitter, making Instagram the preferred platform for promoting conversational exchanges. These findings can be applicable to diverse communicators, subjects, audiences, and environments (online and offline) in initiatives to promote awareness and understanding of science.

Visual Twitter Analytics (Vista)

Purpose

– Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues.

Design/methodology/approach

– Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes.

Findings

– A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be identified and isolated from the large collection of data, without the need to identify these a priori.

Originality/value

– Vista provides mechanisms that support the interactive exploration among Twitter data. By combining automatic data processing and machine learning methods with interactive visualization software, researchers are relieved of tedious data processing tasks, and can focus on the analysis of high-level features of the data. In particular, patterns of Twitter use can be identified, emergent themes can be isolated, and purposeful samples of the data can be selected by the researcher for further analysis.