Social media data for conservation science : A methodological overview

Improved understanding of human-nature interactions is crucial to conservation science and practice, but collecting relevant data remains challenging. Recently, social media have become an increasingly important source of information on human-nature interactions. However, the use of advanced methods for analysing social media is still limited, and social media data are not used to their full potential. In this article, we present available sources of social media data and approaches to mining and analysing these data for conservation science. Specifically, we (i) describe what kind of relevant information can be retrieved from social media platforms, (ii) provide a detailed overview of advanced methods for spatio-temporal, content and network analyses, (iii) exemplify the potential of these approaches for real-world conservation challenges, and (iv) discuss the limitations of social media data analysis in conservation science. Combined with other data sources and carefully considering the biases and ethical issues, social media data can provide a complementary and cost-efficient information source for addressing the grand challenges of biodiversity conservation in the Anthropocene epoch.Peer reviewe

Public engagement of scientists (Science Communication)

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): 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). 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. 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

An evaluative baseline for geo-semantic relatedness and similarity

In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer’s semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. This dataset is larger than existing datasets of this kind, and includes 97 geographic terms combined into 50 term pairs rated by 203 human subjects. GeReSiD is available online and can be used as an evaluation baseline to determine empirically to what degree a given computational model approximates geo-semantic relatedness and similarity

Ciência da Informação: ciência da forma

A disciplina de ciência da informação e da comunicação aparece como um novo campo científico. O que representam as ciências da informação nesse quadro? A idéia que se desenvolve é que ciência da informação consiste em dar forma à mensagem em função de um canal específico e um objetivo fixo da comunicação. Este aspecto, aparentemente de ordem técnica, na realidade diz respeito a pesquisa fundamentai Por exemplo, no campo do documento escrito é o problema da conservação da função da referência extralingüística que interessa.Considerações desse mesmo tipo podem ser feitas a propósito de outras formas de informação (imagens fixas, imagens animadas, emissão radiofônica, impressão de um livro etc.). O domínio da ciência da informação aparece então como um vasto campo ainda inexplorado. Information science: form science Abstract What is the scientific field of information science? In this paper is developed the idea that information science is concerned by adaptation of the form of the message to a given channel and a fixed goal of communication. Such an aspect, which seems very technical, actually concerns fundamental research. For instance, in the field of written documents, it refers to the problem of reference function to the extra-linguistic reality. Consideration of the same kind can be made about other type of message (fixed image, moving image, radio production, etc.) Conseguently, information science appears as a very vast and partly inexplored scientific field

The Broader Impact of Student-Scientist Partnership: Scientists’ Contribution to Students’ Understanding and Proficiencies of Science

This study aims to investigate the broader impacts of student-scientist partnership with an emphasis on scientists’ possible contributions to students’ understanding and proficiencies of science. Appeals from the National Science Foundation have specifically called for broader participation and direct involvement in science and the enhancement of research and education through the linking of scientists with other programs. The Botanical Society of America's PlantingScience project is a partnership of students, science teachers, and scientist-mentors working together in authentic science learning. This dissertation includes three papers. The first paper is an extensive literature review focusing on how scientists can contribute to students’ science learning via online mentoring. The second paper applies a grounded theory approach to build a theory that explains how scientists talk about science when they engage in inquiry activities with students and how this interaction occurs. The third study, which is a mixed methods study, investigates how scientists contribute to students’ science proficiencies and what kind of patterns exist between scientist-mentors and student-teams during inquiry engagement. The literature review reveals an information gap exploring how scientists reflect their understanding of science to K-12 students when they work together in a partnership model. This review pointed out three main questions regarding student-scientist partnerships via online mentoring: (1) What do scientists say about science when they engage in online dialogue about students’ inquiry projects? (2) What are the connections between scientists’ demographics, the subject of the inquiry, and the way they explain the nature of science? and (3) What is the relationship between the quality of students’ inquiries and what their mentors reveal about the nature of science in their dialogues? The results of the grounded theory study revealed the educational, social, and cultural means of the interaction between two parties-- students and scientists. Also, investigation of various cases allowed a better understanding of the essence of nature and culture of science from practitioners’ perspectives. Finally, the mixed methods study revealed that scientists contributed to the authenticity of students’ inquiry experiences by encouraging them to understand scientific explanations, generate scientific evidence with them, reflect on scientific knowledge, and participate productively in scientific discussions

Meeting user needs for sea level rise information: a decision analysis perspective

Despite widespread efforts to implement climate services, there is almost no literature that systematically analyses users' needs. This paper addresses this gap by applying a decision analysis perspective to identify what kind of mean sea‐level rise (SLR) information is needed for local coastal adaptation decisions. We first characterize these decisions, then identify suitable decision analysis approaches and the sea‐level information required, and finally discuss if and how these information needs can be met given the state‐of‐the‐art of sea‐level science. We find that four types of information are needed: i) probabilistic predictions for short term decisions when users are uncertainty tolerant; ii) high‐end and low‐end SLR scenarios chosen for different levels of uncertainty tolerance; iii) upper bounds of SLR for users with a low uncertainty tolerance; and iv) learning scenarios derived from estimating what knowledge will plausibly emerge about SLR over time. Probabilistic predictions can only be attained for the near term (i.e., 2030‐2050) before SLR significantly diverges between low and high emission scenarios, for locations for which modes of climate variability are well understood and the vertical land movement contribution to local sea‐levels is small. Meaningful SLR upper bounds cannot be defined unambiguously from a physical perspective. Low to high‐end scenarios for different levels of uncertainty tolerance, and learning scenarios can be produced, but this involves both expert and user judgments. The decision analysis procedure elaborated here can be applied to other types of climate information that are required for mitigation and adaptation purposes

Official statistics 4.0 - Facts for people in the 21. century

The term ‘statistics’ is used differently; it can refer to a science, a certain kind of information or institutions. Essentially, statistics is the science of learning from data. Certainly, it is a modern technology that is part of the standards of today’s information age and society and is used in a wide array of fields. The history of statistics goes back a long way, accompanying historical eras, technical developments and political turning points just as the census in year zero. Statistics is a method that can reduce complexity, separate signals from noise and distinguish significant from random. The statistical results of this method are used for all conceivable information and decision-making processes. Whether statistics help us better understand the world around us and whether they actually improve decisions (and therefore our lives) is not only a question of scientific methodology. The decisive factor here is whether statistics, like a language, are understood by those for whom the information is relevant. Statistical institutions are the producers of statistics. Using scientific statistical methods, data is collected and existing data is processed in order to calculate condensed information, which is made available to the general public in different forms, such as statistical aggregates, graphics, maps, accounts or indicators. Statistical offices usually belong to the public administration, at state, international, regional or local level. This work is concerned neither with statistics in general nor with the history of theoretical statistics. Rather, the goal is to describe the status quo for a particular area of application, namely ‘official statistics’, based on an analysis of its historical genesis in order then to deploy strategic lines of development for the near future of this particular domain. Central to this work is the quality of statistical information. Statistics can only develop a positive enlightenment effect on the condition that their quality is trusted. To ensure long-term trust in statistics, it is necessary to deal with questions of knowledge, quantification and the function of facts in the social debate. How can we know that we know what we know (or do not know)? The more concrete an answer that can be given to such questions, the more possible it will be to protect statistics against inappropriate expectations and to address false criticism

Examining the use of drama to develop epistemological understanding about the nature of science: A collective case from experience in New Zealand and England, UK

Understanding the nature of science (NoS) is perplexing for young children because it is concerned with not only understanding how evidence is generated but also what kind of meanings can be made from information collected. However, acting as a scientist-in-role, making independent decisions about what information to collect and deciding how to go about it, can enable students to experience scientific practices that empower them to better appreciate and understand the NOS. This paper illustrates how drama processes, in two international settings in Wellington, New Zealand and Oxford, United Kingdom encouraged nine to ten year old children to engage in the scientific ‘as-if’ world. The data collected from these two locations was analysed deductively to illustrate how working-in-role can influence the nature of learning and shape the scientific practices experienced that consequently inform how the NoS is understood. The children in Wellington (New Zealand) worked in-role as atmospheric scientists to design a reduced-emissions race track. The class in Oxford (UK) adopted the role of technological scientists theorising about properties of materials to create and test original carriers designed to transport a range of everyday objects. How drama promoted working-in-role to experience scientific practices supporting the understanding of the NoS, are discussed. The findings suggest that being in-role as a scientist offered learners various opportunities to be agentive, to think and act scientifically, better appreciate the nature of work that scientists do and consequently appreciate the NoS

Educative curricula and improving the science PCK of teachers in middle school settings in rural and remote Australia

Science is one of seven-mandated Key Learning Areas (KLAs) Foundation to Year 10 of the new Australian National Curriculum (ACARA, 2012). Not only, therefore, is science to be offered in every school as part of the curriculum, there is also the expectation that science is to be taught well to all students regardless of location, gender, cultural background or socio-economic status (ACARA, 2012). Studying science provides benefits to individuals by developing their scientific literacy skills (Goodrum, Hackling & Rennie, 2001; Hackling & Prain, 2008). Its study also benefits the national economy by equipping students with the innovative, inventive, and creative skills to generate and apply new ideas as knowledge workers in an interconnected and interdependent global economy (Marginson, Tytler, Freeman & Roberts, 2013; Productivity Commission, 2007). A study of recent literature, including the national and international data on the middle years of school (ACARA, 2012; ACER, 2011, 2013; Goodrum et al., 2001; Goodrum, Druhan, & Abbs, 2012; Hackling & Prain, 2007; Marginson et al., 2013; Office of the Chief Scientist, 2012; Productivity Commission, 2007), could reasonably be expected to show rural and remote students doing well in science if not at least as well as their metropolitan counterparts. Sadly, this is not the case. Science performance in national and international assessments overall is flat-lining (ACARA, 2011; ACER, 2011, 2013) and the gap between metropolitan, rural and remote students in some assessment data indicates as much as 18 months of difference in schooling in favour of metropolitan students and with the gap increasing with increasing remoteness. What are the causes of this inequity and how can it be addressed? Science teachers hold the key (Australian Council of Deans of Science, 2005; Dow, 2003a; Goodrum et al., 2001). Improving the effectiveness of science teachers helps improve science learning outcomes for students. One way to improve the effectiveness of science teachers is to improve their Pedagogical Content Knowledge (Kind, 2009b; Magnusson, Krajcik & Borko, 1999; Loughran, 2010; Loughran, Berry & Mulhall, 2006; Shulman, 1986) through professional learning experiences. However, improving teachers’ science PCK in the middle-school years in rural and remote settings through traditional face-to-face professional learning activities poses a number of challenges. These include lack of casual relief teachers, difficulties in attracting and retaining science teachers, the provision of experienced mentors and coaches and, the provision of fewer professional learning opportunities compared with metropolitan areas (Australian Council of Deans of Science, 2005; Australian Secondary Principal’s Association, 2006; National Centre of Science, Information and Communication Technology, and Mathematics Education for Rural and Regional Australia, 2006). Educative curricula designed to improve teachers’ science PCK as well as learning outcomes for students provide an alternative to traditional face-to-face professional learning for teachers in rural and remote locations (Davis & Krajcik, 2005). Can educative curricula help address the inequity in student science outcomes in rural and remote areas? The Middle Years Astronomy Project (the Project) is an example of one educative curriculum currently in use in the middle years of some rural and remote schools (McKinnon, 2005). This educative curriculum is aligned with the Australian Science Curriculum. It comprises access to telescopes and digital cameras located in NSW (Australia) and Wyoming (USA) that students can control remotely to take photographs of many astronomical phenomena, which can form the basis of further investigations. It also comprises a teachers’ guide designed to improve teachers’ science PCK by providing guidance on designing instructional strategies for science projects with knowledge of five factors in mind. These factors are knowledge of the science content, knowledge of students’ alternative conceptions, knowledge of instructional strategies and the most appropriate assessment strategies to employ, knowledge of the science curriculum, and knowledge of personal beliefs and orientations toward science teaching and learning. This thesis explores the potential for this educative curriculum to improve the PCK of teachers of science in the middle school years in rural and remote settings. It does this by employing a Type IV multiple-case, embedded mixed-methods design (Yin, 2014) over two phases in two states of Australia collecting a range of data from four remote sites in Western Australia and four rural sites in Victoria. Participants comprised 12 teachers, four principals, four teaching principals, one Science KLA Consultant, one Cluster Coordinator and over 200 students. Data were gathered from interviews; archival records; researcher direct observations; an astronomy diagnostic test; student artifacts; and school based documents. A framework, developed from the works of Davis & Krajcik (2005), Kind (2009b) and Magnusson et al. (1999), is used to analyse the data for evidence of changes in teachers’ science PCK. The results of this research indicate that the Project improved teachers’ science PCK for most teachers. Reasons for this are presented. An emerging phenomenon from the research was the ability of experienced science teachers to move holistically and fluidly between components of PCK to make in the moment pedagogical decisions to improve student learning. This has been referred to as ‘pinball pedagogical reasoning’ (Mitchell, Pannizon, Keast & Loughran, 2015). The findings of this research have implications for both current practice and future research, providing guidance to teachers and designers of professional learning experiences, including educative curriculum designers, on the areas to target when seeking to develop components of PCK for experienced teachers and on assisting less experienced teachers to acquire the ‘pinball pedagogical reasoning’ skills of experienced teachers. The findings also suggest that PCK development takes time and requires a planned and systematic approach to teacher career development with support from the employer. This thesis suggests further areas for research and concludes by arguing that a poor science education, which results in poorer scientific literacy skills and a reduced ability to contribute to, and thrive in, the national and international knowledge economies, adds to the education disadvantage students in rural and remote locations experience relative to their metropolitan peers. It advocates a moral imperative to ensure this does not happen. It also suggests that using educative curricula to improve the PCK of rural and remote science teacher, as well as science student learning outcomes, is a strategy worthy of pursuit