MEDIT Open Seminar by Annette Markham and Somayeh Labafi

In professor Annette Markham´s paper, she sketches early findings from her recent ethnomethodological study of the human-machine interactions between Spot, the agile and semi-autonomous Boston Dynamics robot that resembles a dog without a head. Because this robot tends to provoke strong reactions, whether positive or negative, focusing on the micro moment of the initial encounter enabled the research team to identify a rapid sequence of ontological disruptures and repairs. Annette shares some snippets to showcase the robot as a boundary object and highlight the dynamic/dialectic processes of sensemaking. This study is part of Annette’s larger project to analyze how datafication and digitalization impact identity and sociality. As robotics and AI continue to converge, the granularities of human-machine communication processes become ever more salient, raising many ethical questions about critically analyzing and governing AI design.

Annette Markham is Professor of Media and Communication and Director of the Digital Ethnography Research Centre at RMIT University, Australia. She is a pioneering researcher of digital culture and has been researching the impact of digitalization on identity and organizing practices since 1995. Annette holds specializations in the lived experience of human/machine interactions, impact of datafication and algorithmic logics on social practices, and critical approaches to digital and algorithmic identity. She is one of the world’s top experts in ethical frameworks for digital social research and is well known for her longstanding work to develop innovative methods for mixed-method and ethnographic research in digitally-saturated contexts. Annette created and directed the international Future Making Research Consortium in 2016, a collaboratory to bring together scholars, artists, and activists, particularly early career researchers, to study the intersection of digital technology, ways of being in the world, and future possible meanings, practices, and social structures. She regularly hosts post-graduate workshops, an annual Skagen Institute Conference on Transgressive Methods, PhD summer schools, and special interest masterclasses. Annette’s writing can be found in a range of international journals, books, and edited collections.

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Evidence-based policy seeks to use evidence in public policy in a systematic way in a bid to improve decision-making quality. Evidence-based policy cannot work properly and achieve the expected results without accurate, appropriate, and sufficient evidence. Given the prevalence of social media and intense user engagement, the question to ask is whether the data on social media can be used as evidence in the policy-making process. The question gives rise to the debate on what characteristics of data should be considered as evidence. 
Despite the numerous research studies carried out on social media analysis or policy-making, this domain has not been dealt with through an “evidence detection” lens. Thus, this study addresses the gap in the literature on how to analyze the big text data produced by social media and how to use it for policy-making based on evidence detection. This research seeks to fill the gap by developing and offering a model that can help policy-makers to distinguish “evidence” from “non-evidence”. To do so, in the first phase of the study, the researchers elicited the characteristics of the “evidence” by conducting a thematic analysis of semi-structured interviews with experts and policy-makers. In the second phase, the developed model was tested against 6-month data elicited from Twitter accounts. The experimental results show that the evidence detection model performed better with decision tree (DT) than the other algorithms. Decision tree (DT) outperformed the other algorithms by an 85.9% accuracy score. 
This study shows how the model managed to fulfill the aim of the present study, which was detecting Twitter posts that can be used as evidence. This study contributes to the body of knowledge by exploring novel models of text processing and offering an efficient method for analyzing big text data. The practical implication of the study also lies in its efficiency and ease of use, which offers the required evidence for policy-makers. 

Somayeh Labafi is Assistant Professor of Media Management at Iranian Research Institute for Information Science and Technology (IranDoc). Somayeh serves as the lecturer at University of Tehran and as head of Social Media and Data Governance Laboratory. So far, she has taught courses such as social network management, media management and media policy at University of Tehran. 
Somayeh earned her master's and doctorate degrees in media management from the University of Tehran. She is also a member of the editorial board of Information Processing & Management Quarterly and Media Management Review. Her research is mostly concerned with social media and media policy. Her research areas of interest include social media, media policy, and network analysis methods. She welcomes international collaboration and participation in projects.