Public engagement of scientists (Science Communication)

Abstract

Public engagement of scientists is defined as “all kinds of publicly accessible communication carried out by people presenting themselves as scientists. This includes scholarly communication directed at peers as well as science communication directed at lay publics” (Jünger & Fähnrich, 2019, p. 7).

Field of application/theoretical foundation:

The variable “public engagement of scientists” can be differentiated according to the following three main dimensions (Jünger & Fähnrich, 2019):

1. Directions of engagement: Describes the extent to which communication scientists on Twitter connect with people from different sectors of society (e.g. science, politics, media, economy). This allows conclusions to the potential influence of scientists reaching specific audiences beyond the scientific community (Jünger & Fähnrich, 2019).
2. Topics of engagement: Previous research reveals that social scientists not only act as experts in their research field, but often present themselves as public intellectuals by also referring to political and social issues (Albæk, Christiansen, & Togeby, 2003; Fähnrich & Lüthje, 2017). For this reason, communication scientists are expected to communicate not only on scientific but also on political or economic issues.
3. Modes of engagement: In addition to disseminating information, social networking sites also allow for more interactive ways of maintaining relationships. Thus, following Ellison and Boyd (2013), it can be assumed that communication on social networking sites can be both content-centered and user-centered. This dimension can be linked to the speech act theory (Klemm, 2000; Searle, 1990), according to which every use of language has a performative function.

References/combination with other methods of data collection:

In some cases, a mixed method approach, employing two data collection methods, is applied: a content analysis is complemented by a survey to gain information about the science communicators such as demographic information (Hara, Abbazio, & Perkins, 2019). Furthermore, their social networks are investigated by means of network analysis (Walter, Lörcher, & Brüggemann, 2019).

Example studies:

Hara et al. (2019); Jahng & Lee (2018); Kouper (2010); Mahrt & Puschmann (2014); Walter et al. (2019)

Information on Jünger & Fähnrich, 2019

Authors: Jakob Jünger & Birte Fähnrich, 2019

Research questions: How can the public engagement of scientists in the context of online communication be conceptualized? Which types of engagement occur in the Twitter activity of communication scholars?

Object of analysis: Tweets and followers belonging to the Twitter profiles of communication scientists who are following the International Communication Association (ICA) on Twitter (only German- and English-speaking users)

Timeframe of analysis: Data collection in September 2017

Info about variables

Variable name/definition: Subject area of the content of the tweets

Level of analysis: Tweet

Values:

- Science-related topics (research, teaching)

- Non-scientific topics (politics, economy, media, sports, environment, society, leisure time, and others)

Scale of measurement: Nominal

Reliability: Gwet’s AC1: 0,71 – 1,00; Holsti: 0,82 – 1,00

 

Variable name/definition: Language patterns of communication scientists (Speech acts)

Level of analysis: Tweet

Values:

- Actor-centered patterns (discussing, activating, socializing),

- Content-centered patterns (reporting, commenting),

- Other language patterns

Scale of measurement: Nominal

Reliability: Gwet’s AC1: 0,54 – 0,95; Holsti: 0,75 – 1,00

 

Variable name/definition: References of the communication scientists on Twitter

Level of analysis: Tweet

Values:

- Self-reference,

- Reference to specific actor,

- Reference to other unspecific actor,

- No reference to actors

Scale of measurement: Nominal

Reliability: Gwet’s AC1: 0,83 – 0,87; Holsti: 0,88 – 0,93

 

Variable name/definition: Type of actor (followers of the investigated scientists)

Level of analysis: Self description in profile

Values: Person, Organization

Scale of measurement: Nominal

Reliability: Gwet’s AC1: 0,89; Holsti: 0,91; Kappa: 0,84; Krippendorffs’ Alpha: 0,84

 

Variable name/definition: Social sphere of action of the followers

Level of analysis: Self description in profile

Values:

- Science (communication science, other sciences, science in general)

- Politics (party, state/administration, activists & lobbyists)

- Media (media & journalism, news & comments)

- Economy (communication industry, other economic sectors)

- Arts & Entertainment

- Health

- Other (Other areas of activity, personal interests)

Scale of measurement: Nominal

Reliability: Gwet’s AC1: 0,81 – 0,87; Holsti: 0,82 – 0,88; Kappa: 0,83 – 0,85; Krippendorffs’ Alpha: 0,83 – 0,85

Codebook: in the appendix (in German)

Information on Walter, Lörcher & Brüggemann, 2019

Authors: Stefanie Walter, Ines Lörcher & Michael Brüggemann

Research question: How do scientists interact with politicians and civil society on Twitter?

Object of analysis: Climate-related English-language Tweets posted by scientists from the United States (to classify the Twitter users, an automated content analysis, a dictionary approach, was applied; Krippendorffs’ Alpha: 0,74)

Timeframe of analysis: Data collection took place from October 1, 2017 to March 31, 2018

Variable name/definition: Mode and content of communication

Level of analysis: Tweet

Values: Negative emotion, Certainty

Scale of measurement: Linguistic Inquiry and Word Count (LIWC) program for computerized text analysis

Reliability: –

Codebook: in the appendix (R-Script)

Information on Hara et al., 2019

Authors: Noriko Hara, Jessica Abbazio & Kathryn Perkins

Research questions: What kind of demographic characteristics do the scientists participating in “Science” subreddit AMAs have? [survey] What was the experience like to host an AMA in the “Science” subreddit? [survey] What type of discussions did “Science” subreddit AMA participants engage in? Do questions receive answers? What are posters’ intentions? What kind of content features appear? Who is posting comments? What kind of responses do posts receive?

Object of analysis: Six Ask Me Anything (AMA) sessions on Reddit’s “Science” subreddit (r/science)

Timeframe of analysis: –

Info about variable

Variable name/definition: Poster’s intentions (PI); Answer status (AS); Comment status (CS); Poster’s identity (PID); Content features (CF)

Level of analysis: Post

Values: 

- PI: Seeking information, Seeking discussion, Non-questions/comments, Further discussion/interaction among users, Answering a question

- AS: Answered, Not answered

- CS: Commented on, Not commented on

- PID: Host, Participant – flair, Participant – no flair

- CF: Providing factual information, Providing opinions, Providing resources, Providing personal experience, Providing guidance on forum governance, Making an inquiry – initial question, Making an inquiry – embedded question, Requesting resources, Off-topic comment

Scale of measurement: Nominal

Reliability: Intercoder reliability ranged between 0.66 and 1.0 calculated by Cohen’s Kappa

Codebook: in the appendix (in English)

References

Albæk, E., Christiansen, P. M., & Togeby, L. (2003). Experts in the mass media: Researchers as sources in Danish daily newspapers, 1961–2001. Journalism & Mass Communication Quarterly, 80(4), 937–948.

Ellison, N. B., & Boyd, D. M. (2013). Sociality through social network sites. In W. H. Dutton, N. B. Ellison, & D. M. Boyd (Eds.), The Oxford Handbook of Internet Studies (pp. 151–172). Oxford: Oxford University Press.

Fähnrich, B., & Lüthje, C. (2017). Roles of Social Scientists in Crisis Media Reporting: The Case of the German Populist Radical Right Movement PEGIDA. Science Communication, 39(4), 415–442.

Hara, N., Abbazio, J., & Perkins, K. (2019). An emerging form of public engagement with science: Ask Me Anything (AMA) sessions on Reddit r/science. PloS One, 14(5), e0216789.

Jahng, M. R., & Lee, N. (2018). When scientists tweet for social changes: Dialogic communication and collective mobilization strategies by flint water study scientists on Twitter. Science Communication, 40(1), 89–108. https://doi.org/10.1177/1075547017751948

Jünger, J., & Fähnrich, B. (2019). Does really no one care?: Analyzing the public engagement of communication scientists on Twitter. New Media & Society, 7(2), 146144481986341.

Klemm, M. (2000). Zuschauerkommunikation: Formen und Funktionen der alltäglichen kommunikativen Fernsehaneignung [Audience Communication: Forms and Functions of Everyday Communicative Appropriation of Television]. Frankfurt am Main: Lang.

Kouper, I. (2010). Science blogs and public engagement with science: Practices, challenges, and opportunities. Journal of Science Communication, 09(01).

Mahrt, M., & Puschmann, C. (2014). Science blogging: An exploratory study of motives, styles, and audience reactions. Journal of Science Communication, 13(03).

Searle, J. R. (1990). Sprechakte: Ein sprachphilosophischer Essay [Speech Acts: An Essay on the Philosophy of Language]. Frankfurt am Main: Suhrkamp.

Walter, S., Lörcher, I., & Brüggemann, M. (2019). Scientific networks on Twitter: Analyzing scientists’ interactions in the climate change debate. Public Understanding of Science, 28(6), 696–712.