Posts by saul

I have put my introduction to digital transcription workshop materials and tutorials online, here’s a little blog outlining some of the reasons I started developing the workshop, and how I hope researchers will use it.

There are very few – if any – software tools designed specifically for conversation analytic transcription, partly because so few conversation analysts use them, so there’s not really a ‘market’ for software developers to cater to.

Instead, we have to make do with tools that were designed for more generic research workflows, and which often build in analytic assumptions, constraints and visual metaphors that don’t necessarily correspond with EM/CA’s methodological priorities.

Nonetheless, most researchers that use digital transcription systems choose between two main paradigms.

  1. the ‘list-of-turns’-type system represents interaction much like a Jeffersonian transcript: a rendering of turn-by-turn talk, line by line, laid out semi-diagrammatically so that lines of overlapping talk are vertically aligned on the page.
  2. the ‘tiers-of-timelines’ system uses a horizontal scrolling timeline like a video editing interface, with multiple layers or ‘tiers’ representing e.g., each participant’s talk, embodied actions, and other types of action annotated over time.


A key utility of both kinds of digital transcription systems is that they allow researchers to align media and transcript, and to use very precise timing tools to check the order and timing of their analytic observations.

I used these terms to describe this distinction between representational schema in a short ‘expert box’ for Alexa Hepburn and Galina Bolden’s excellent (2017) book Transcribing for Social Research entitled “how to choose transcription software for conversation analysis“, where I tried to explain what is at stake in choosing one or the other type of system .

For the most part, researchers choose lists-of-turns tools when their analysis is focused on conversation and audible turn-space, and tiers-of-timelines when their analysis focuses on video analysis of visible bodily action.

The problem for EM/CA researchers working with both these approaches, however, is that neither representational schema on its own, (nor any schema save whatever schema may have been constituted through the original interaction itself), is ideal for exploring and describing participants’ sense-making processes and resources.

Tiers-of-timelines representations are great for showing the temporal unfolding of simultaneous action, but it is hard to read more than a few seconds of activity at a glance. By contrast, lists-of-turns use the same basic schema as our well-practiced, mundane reading abilities to scan a page of text and take in the overall structure of a conversation, but reduce the fine-grained timing and multi-activity organization of complex embodied activities.

In any case, neither of these representational schema, nor any currently available transcription tools adequately capture the dynamics of movement in the way that, for example, specialized graphical methods and life drawing techniques were developed to achieve (although our Drawing Interactions prototype points to some possibilities).

The reason I put this digital transcription workshop together was to combine existing, well-used software tools for digital transcription from both major paradigms, and to show how to work on a piece of data using both approaches. It’s not intended as a comprehensive ‘solution’, and there are many unresolved practical and conceptual issues, but I think it gives researchers the best chance to address their empirical concerns to help break away from the conceptual and disciplinary constraints that come from analyzing data using one, uniform type of user interface.

The workshop materials include slides (so people can use them to teach collaborators/students) as well as a series of short tutorial videos accompanying each practical exercise in the slides, along with some commentary from me.

My hope is that researchers will use and improve these materials, and possibly extend them to include additional tools (e.g., EXMARaLDA project tools, with which I’m less familiar). If you do, and you find ways to improve them with additional tips, hacks, or updated instructions that take into account new versions, please do let me know.

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I gave this keynote at the first European Conference on Conversation Analysis (ECCA 2020), which, due to C-19, had to be delivered as a video instead of a stand-up talk.

I tried to make a mix between a film essay and a research presentation of work-in-progress, so it didn’t always work to put references on every slide. I’ve added them below with links to the data used where available.


Sacks’ (1963) first published paper on ‘sociological description’ uses the metaphor of a mysterious ‘talking-and-doing’ machine, where researchers from different disciplines come up with incompatible, contradictory descriptions of its functionality. We may soon find ourselves in a similar situation to the one Sacks describes as AI continues to permeate the social sciences, and CA begins to encounter AI either as a research object, as a research tool, or more likely as a pervasive feature of both.

There is now a thriving industry in ‘Conversational AI’ and AI-based tools that claim to emulate or analyse talk, but both the study and use of AI within CA is still unusual. While a growing literature is using CA to study social robotics, voice interfaces, and  conversational user experience design (Pelikan & Broth, 2016; Porcheron et al., 2018), few conversation analysts even use digital tools, let alone the statistical and computational methods that underpin conversational AI. Similarly, researchers and developers of conversational AI rarely cite CA research and have only recently become interested in CA as a possible solution to hard problems in natural language processing (NLP). This situation presents an opportunity for mutual engagement between conversational AI and CA (Housley et al., 2019). To prompt a debate on this issue, I will present three projects that combine AI and CA very differently and discusses the implications and possibilities for combined research programmes.

The first project uses a series of single case analyses to explore recordings in which an advanced conversational AI successfully makes appointments over the phone with a human call-taker. The second revisits debates on using automated speech recognition for CA transcription (Moore, 2015) in light of significant recent advances in AI-based speech-to-text, and includes a live demo of ‘Gailbot’, a Jeffersonian automated transcription system. The third project both uses and studies AI in an applied CA context. Using video analysis, it asks how a disabled man and his care worker interact while using AI-based voice interfaces and a co-designed ‘home automation’ system as part of a domestic routine of waking, eating, and personal care. Data are drawn from a corpus of ~500 hours of video data recorded by the participants using a voice-controlled, AI-based ‘smart security camera’ system.

These three examples of CA’s potential interpretations and uses of AI’s ‘talking-and-doing’ machines provide material for a debate about how CA research programmes might conceptualize AI, and use or combine it with CA in a mutually informative way.

Videos (in order of appearance)

The Senster. (2007, March 29).

MIT AI Lab. (2011, September 25).

Keynote (Google I/O ’18). (2018, May 9).

Online Data

Linguistic Data Consortium. (2013). CABank CallHome English Corpus [Data set]. Talkbank.

Jefferson, G. (2007). CABank English Jefferson NB Corpus [Data set]. TalkBank.


Agre, P. (1997). Toward a critical technical practice: Lessons learned in trying to reform AI. Social Science, Technical Systems and Cooperative Work: Beyond the Great Divide. Erlbaum.

Alač, M., Gluzman, Y., Aflatoun, T., Bari, A., Jing, B., & Mozqueda, G. (2020). How Everyday Interactions with Digital Voice Assistants Resist a Return to the Individual. Evental Aesthetics, 9(1), 51.

Berger, I., Viney, R., & Rae, J. P. (2016). Do continuing states of incipient talk exist? Journal of Pragmatics, 91, 29–44.

Bolden, G. B. (2015). Transcribing as Research: “Manual” Transcription and Conversation Analysis. Research on Language and Social Interaction, 48(3), 276–280.

Brooker, P., Dutton, W., & Mair, M. (2019). The new ghosts in the machine: “Pragmatist” AI and the conceptual perils of anthropomorphic description. Ethnographic Studies, 16, 272–298.

Button, Graham. (1990). Going Up a Blind Alley: Conflating Conversation Analysis and Computational Modelling. In P. Luff, N. Gilbert, & D. Frolich (Eds.), Computers and Conversation (pp. 67–90). Academic Press.

Button, Graham, & Dourish, P. (1996). Technomethodology: Paradoxes and possibilities. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.

Button, G., & Sharrock, W. (1996). Project work: The organisation of collaborative design and development in software engineering. Computer Supported Cooperative Work (CSCW), 5(4), 369–386.

Casino, T., & Freenor, Michael. (2018). An introduction to Google Duplex and natural conversations, Willowtree.

Duca, D. (2019). Who’s disrupting transcription in academia? — SAGE Ocean | Big Data, New Tech, Social Science. SAGE Ocean.

Fischer, J. E., Reeves, S., Porcheron, M., & Sikveland, R. O. (2019). Progressivity for voice interface design. Proceedings of the 1st International Conference on Conversational User Interfaces  – CUI ’19, 1–8.

Garfinkel, H. (1967). Studies in ethnomethodology. Prentice-Hall.

Goodwin, C. (1996). Transparent vision. In E. A. Schegloff & S. A. Thompson (Eds.), Interaction and Grammar (pp. 370–404). Cambridge University Press.

Heath, C., & Luff, P. (1992). Collaboration and control: Crisis management and multimedia technology in London Underground Line Control Rooms. Computer Supported Cooperative Work (CSCW), 1(1–2), 69–94.

Heritage, J. (1984). Garfinkel and ethnomethodology. Polity Press.

Heritage, J. (1988). Explanations as accounts: A conversation analytic perspective. In C. Antaki (Ed.), Analysing Everyday Explanation: A Casebook of Methods (pp. 127–144). Sage Publications.

Hoey, E. M. (2017). Lapse organization in interaction [PhD Thesis, Max Planck Institute for Psycholinguistics, Radbound University, Nijmegen].

Housley, W., Albert, S., & Stokoe, E. (2019). Natural Action Processing. In J. E. Fischer, S. Martindale, M. Porcheron, S. Reeves, & J. Spence (Eds.), Proceedings of the Halfway to the Future Symposium 2019 (pp. 1–4). Association for Computing Machinery.

Kendrick, K. H. (2017). Using Conversation Analysis in the Lab. Research on Language and Social Interaction, 50(1), 1–11.

Lee, S.-H. (2006). Second summonings in Korean telephone conversation openings. Language in Society, 35(02).

Leviathan, Y., & Matias, Y. (2018). Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone [Blog]. Google AI Blog.

Local, J., & Walker, G. (2005). Methodological Imperatives for Investigating the Phonetic Organization and Phonological Structures of Spontaneous Speech. Phonetica, 62(2–4), 120–130.

Luff, P., Gilbert, N., & Frolich, D. (Eds.). (1990). Computers and Conversation. Academic Press.

Moore, R. J. (2015). Automated Transcription and Conversation Analysis. Research on Language and Social Interaction, 48(3), 253–270.

Ogden, R. (2015). Data Always Invite Us to Listen Again: Arguments for Mixing Our Methods. Research on Language and Social Interaction, 48(3), 271–275.

O’Leary, D. E. (2019). Google’s Duplex: Pretending to be human. Intelligent Systems in Accounting, Finance and Management, 26(1), 46–53.

Pelikan, H. R. M., & Broth, M. (2016). Why That Nao? Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems – CHI \textquotesingle16.

Pelikan, H. R. M., Broth, M., & Keevallik, L. (2020). “Are You Sad, Cozmo?”: How Humans Make Sense of a Home Robot’s Emotion Displays. Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 461–470.

Porcheron, M., Fischer, J. E., Reeves, S., & Sharples, S. (2018). Voice Interfaces in Everyday Life. Proceedings of the 2018 ACM Conference on Human Factors in Computing Systems (CHI’18).

Reeves, S. (2017). Some conversational challenges of talking with machines. Talking with Conversational Agents in Collaborative Action, Workshop at the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing.

Relieu, M., Sahin, M., & Francillon, A. (2019). Lenny the bot as a resource for sequential analysis: Exploring the treatment of Next Turn Repair Initiation in the beginnings of unsolicited calls.

Robles, J. S., DiDomenico, S., & Raclaw, J. (2018). Doing being an ordinary technology and social media user. Language & Communication, 60, 150–167.

Sacks, H. (1984). On doing “being ordinary.” In J. Heritage & J. M. Atkinson (Eds.), Structures of social action: Studies in conversation analysis (pp. 413–429). Cambridge University Press.

Sacks, H. (1987). On the preferences for agreement and contiguity in sequences in conversation. In G Button & J. R. Lee (Eds.), Talk and social organization (pp. 54–69). Multilingual Matters.

Sacks, H. (1995a). Lectures on conversation: Vol. II (G. Jefferson, Ed.). Wiley-Blackwell.

Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.

Sahin, M., Relieu, M., & Francillon, A. (2017). Using chatbots against voice spam: Analyzing Lenny’s effectiveness. Proceedings of the Thirteenth Symposium on Usable Privacy and Security, 319–337.

Schegloff, E. A. (1988). On an Actual Virtual Servo-Mechanism for Guessing Bad News: A Single Case Conjecture. Social Problems, 35(4), 442–457.

Schegloff, E. A. (1993). Reflections on Quantification in the Study of Conversation. Research on Language & Social Interaction, 26(1), 99–128.

Schegloff, E. A. (2004). Answering the Phone. In G. H. Lerner (Ed.), Conversation Analysis: Studies from the First Generation (pp. 63–109). John Benjamins Publishing Company.

Schegloff, E. A. (2010). Some Other “Uh(m)s.” Discourse Processes, 47(2), 130–174.

Soltau, H., Saon, G., & Kingsbury, B. (2010). The IBM Attila speech recognition toolkit. 2010 IEEE Spoken Language Technology Workshop, 97–102.

Stivers, T. (2015). Coding Social Interaction: A Heretical Approach in Conversation Analysis? Research on Language and Social Interaction, 48(1), 1–19.

Stokoe, E. (2011). Simulated Interaction and Communication Skills Training: The `Conversation-Analytic Role-Play Method’. In Applied Conversation Analysis (pp. 119–139). Palgrave Macmillan UK.

Stokoe, E. (2013). The (In)Authenticity of Simulated Talk: Comparing Role-Played and Actual Interaction and the Implications for Communication Training. Research on Language & Social Interaction, 46(2), 165–185.

Stokoe, E. (2014). The Conversation Analytic Role-play Method (CARM): A Method for Training Communication Skills as an Alternative to Simulated Role-play. Research on Language and Social Interaction, 47(3), 255–265.

Stokoe, E., Sikveland, R. O., Albert, S., Hamann, M., & Housley, W. (2020). Can humans simulate talking like other humans? Comparing simulated clients to real customers in service inquiries. Discourse Studies, 22(1), 87–109.

Turing, A. (1950). Computing machinery and intelligence. Mind, 49, 433–460.

Walker, G. (2017). Pitch and the Projection of More Talk. Research on Language and Social Interaction, 50(2), 206–225.

Wong, J. C. (2019, May 29). “A white-collar sweatshop”: Google Assistant contractors allege wage theft. The Guardian.


I’ve been fascinated by a live camera stream showing a UK street since the start of the lockdown on the 23rd March 2020 because it’s shown how pedestrians interpret the 2m physical distancing rule.

Some of the data from this camera was incorporated into a very nice ROLSI blog post by Eric Laurier, Magnus Hamann and Liz Stokoe that I helped with about the emergence of the ‘social swerve’.

I thought others might find it useful to read a quick how-to about grabbing video from live cameras – it’s a great way to get a quick and dirty bit of data to test a working hunch or do some rough analysis.

There are thousands of live cameras that stream to youtube, but it can be a bit cumbersome to capture more than a few seconds via more straightforward screen capture methods.

NB: before doing this for research purposes, check that doing so is compliant with relevant regional/institutional ethical guidelines.

Step 1: download and configure youtube-dl

Youtube-dl is a command line utility, which means you run it from the terminal window of your operating system of choice – it works fine on any Unix, on Windows or on Mac Os.

Don’t be intimidated if you’ve never used a command line before, you won’t have to do much beyond some copying and pasting.

I can’t do an installation how-to, but there are plenty online:



I’ll assume that if you’re a Unix user, you know how to do this.

Step 2: copy and paste the video ID from the stream

Every Youtube video has a video ID that you can copy from the address bar of your browser. Here’s the one I used for the blog post mentioned above – which we’ve affectionately nicknamed the ‘kebab corpus’. The video ID is circled in red:

Step 3: use youtube-dl to begin gathering your video data

This bit is a little hacky – as in not really using the software as intended or documented, so I’ve created a short howto video. There might be better ways. If so, please let me know!

As I mention in that video – probably best not to leave youtube-dl running for too long on a stream as you might end up losing your video if something happens to interrupt the stream. I’ve captured up to half an hour at a time.

It’s possible to create scripts and automated actions for a variety of operating systems to do this all for you on a schedule – but if you need extensive video archives, I’d recommend contacting the owner of the stream to see if they can simply send you their high quality youtube archives.

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We have a a fully funded PhD position available (deadline 6th March 2020) to work with myself, Prof. Charles Antaki and Prof. Liz Peel in collaboration with The Alzheimer’s Society to explore the opportunities, risks and wider issues surrounding the use of AI-based voice technologies such as the Amazon Echo and home automation systems in the lives of people with dementia.

Voice technologies are often marketed as enabling people’s independence. For example, Amazon’s “Sharing is Caring” advert for its AI-based voice assistant Alexa shows an elderly man being taught to use the ‘remind me’ function of an Amazon Echo smart speaker by his young carer. But how accessible are these technologies in practice? How are people with dementia and carers using them in creative ways to solve everyday access issues? And what are the implications for policy given the consent and privacy issues?

The project will combine micro and macro-levels of analysis and research. On the micro-level, the successful applicant will be trained and/or supported to use video analysis to study how people with dementia collaborate with their assistants to adapt and use voice technologies to solve everyday access issues. On the macro-level, the project will involve working on larger scale operations and policy issues with Ian Mcreath and Hannah Brayford at The Alzheimer’s Society and within the wider Dementia Choices Action Network (#DCAN).

Through this collaboration, the research will influence how new technologies are used, interpreted and integrated into personalised care planning across health, social care and voluntary, community and social enterprise sectors.

The deadline is the 6th March 2020 (see the job ad for application details). All you need to submit for a first round application is a CV and a short form, with a brief personal statement. We welcome applications from people from all backgrounds and levels of research experience (training in specific research methods will be provided where necessary). We especially welcome applications from people with first hand experience of disability and dementia, or with experience of working as a formal or informal carer/personal assistant.

This research will form part of the Adept at Adaptation project, looking at how disabled people adapt consumer AI-based voice technologies to support their independence across a wide range of impairment groups and applied settings.

The successful applicant will be supported through the ESRC Midlands Doctoral Training Partnership, and will have access to a range of highly relevant supervision and training through the Centre for Research in Communication and Culture at Loughborough University.

Feel free to contact me on with any informal inquiries about the post.

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I recently wrote this congratulatory email to the wonderful admins of the Ethnomethodology and Conversation Analysis Wiki ( to congratulate them on reaching a real milestone in this community project. We don’t really have a place to share these things yet so I’m putting it here.

If you are reading this and would like to get involved in the wiki or related projects mentioned here, please drop me an email or message me on Twitter.

Dear Paul and the EMCA wiki team,

I can’t quite believe it’s been six years since we started the EMCAwiki project – when Paul sent out an email via the languse mailing list asking for help with his original EM/CA news site and we began the discussions that led to the lovely bibliography wiki we now run.

Towards the end of 2019 we finally completed the transfer of all remaining legacy bibliography entries to the new wiki format from Paul’s original very long PDF files. We now host a grand total of 8537 entries – from our first entry: (Harold Garfinkel, (1949), “Research Note on Inter- and Intraracial Homicides”, Social Forces, vol. 27, no. 4, pp. 369–381.) – to a host of new papers published as recently as this first week of 2020. We can now begin consolidating and standardizing our work (as Andrei Korbut has been doing brilliantly over the last few months) – making sure things are consistent, and then thinking about how to explore, analyze and share the EM/CA bibliometric data we now have at our disposal. I’ll write more about that below – but this is quite an achievement, and I’m very grateful to all of you, for putting in such incredibly generous and dedicated work.

Before I say anything more or propose any new projects or initiatives, I should say that one of the things I like most about the EM/CA wiki is the almost total lack of administrative overheads. So many things in academic life are bogged down with committees, meetings, action items etc… I love the fact that from the beginning we’ve not really done that, but have mostly just got on with the tasks we thought necessary to the best of our individual and collective abilities. We’ve sometimes made efforts to met up at conferences, which has been fun, and have continued to take the pragmatic approach of just doing what we can when possible without undue pressure or overarching expectations. This is outstanding, and long may it continue.

Having said that, I did take on a new role in 2019 – that of ISCA communications & information officer, and I now participate in more admin meetings than I would usually aim for. These are great fun, and some have included ideas that involve EMCA wiki. I wanted to share some of those ideas with you now, and leave it open to you all to respond (or not) in what is now a time-honored laid-back tradition of the EMCAwiki admins.

Firstly, I am aware that the reason I was elected to the ISCA board was because of this project and all of your work. I would like to acknowledge that publicly by adding a page to the new ISCA website I’m currently developing – aiming to launch it towards the end of January 2020. I have kept a list of admins here: – I like the fact that we have all done different things at different times – and some of us have been more active than others. I hope that continues. If you would really prefer not to be acknowledged for what you’ve done – or what you may do in the future – let me know.

Secondly, I am working with Lucas Seuren and a great group of ECRs from around the world on an exciting new ISCA project. This will draw on the content in the EMCA wiki and promote it to a wider audience, as well as inviting contributions beyond bibliography entries (e.g. lists of up-to-date equipment, cross-cultural ethics frameworks for data recording, shared syllabi, useful youtube videos etc.). I hope that this will contribute positively to the wiki, without increasing any administrative overhead. Of course if any of you would like to contribute to that project too, please let me know.

Thirdly, I am aware that there are lots of features of the wiki that I have long promised to implement – and I have long delayed that implementation. I’ve written many of them down here: There is also a list of ‘known issues’ that have been pointed out as problems over the years: – I’m going to acknowledge now that I doubt I’ll ever have time to implement any of these myself. I have fewer and fewer of the kind of uninterrupted stretches of code hacking-time that are required for software development. Instead, I’m going to try to raise funds to pay professional programmers and systems administrators to do this. I think it’s something I could find a funder to support, and I’ll work on this – with your consent – and (if you have any ideas/time/funders) your involvement and collaboration.

I hope all of that sounds OK. I’m just going to get on with it slowly, and will welcome any thoughts/feedback/initiatives/and ideas that you all have over the next decade.

All the best, and happy 2020,

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Image from the 2018 Drawing Interactions workshop at the University of Liverpool, London

On the 28th November 2019 I’m running this workshop in London for the National Centre for Research Methods with Pat Healey, Matthew Tobias Harris, Claude Heath, and Sophie Skach, which focuses on drawing as a method in interaction analysis. It’s open to any researcher and/or draftsperson – regardless of experience with conversation analysis or drawing. The aim is to introduce artists and social scientists to each other’s methods for visual analysis, inductive observation and inscription of research objects. Places are limited, so please sign up at the link below:

Workshop abstract

Analysing embodied interaction enables researchers to study the qualitative details of communication and to do reliable coding of interaction for quantification. Some researchers use video stills and word processing software to add arrows and highlights. Others use simple sketches or tracings to present their research findings in their final published results. However, until now, no dedicated courses have been offered that teach drawing as a method for the transcription and analysis of social interaction.

This one-day course will introduce researchers to the theory and method of conversation analysis, and to new graphical tools, transcription methods, and software systems that are available for multimodal analysis of audio-visual data. It will involve short presentations, group discussions and practical work including video data gathering, transcription and analysis. No special equipment is required, although we encourage participants to bring some means of recording video (e.g. a phone or other digital camera).

This course is aimed at researchers across disciplines with an interest in face-to-face social interaction and communication (human or animal, face-to-face or video-mediated). No prior experience of drawing or conversation and discourse analysis is necessary, since we will cover the basics required to learn independently.

Learning outcomes

This course will introduce you to methods, techniques and tools for analysing embodied social interaction.

The course covers:

  • Conversation analytic methods for collecting, transcribing and analysing video data.
  • Drawing techniques for use in field notes and in exploratory data analysis sessions.
  • How to create and use multimodal transcripts for data analysis and presentation of results.
  • Software tools for creating and sharing computer-readable graphical transcriptions.
  • Future directions for multimodal interaction analytics e.g. automation and open science.
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As part of the Drawing Interactions project (see report), Pat HealeyToby Harris, Claude Heath, and Sophie Skach and I ran a workshop at New Developments in Ethnomethodology in London (March 2018) to teach interaction analysts how and why to draw.

Sophie Skach leading the life drawing workshop at New Directions in Ethnomethodology

Sophie Skach leading the workshop at New Directions in Ethnomethodology

Here’s the workshop abstract:

Ethnomethodological and conversation analytic (EM/CA) studies often use video software for transcription, analysis and presentation, but no such tools are designed specifically for EM/CA. There are, however, many software tools commonly used to support EM/CA research processes (Hepburn & Bolden, 2016 pp. 152-169; Heath, Hindmarsh & Luff 2010 pp. 109-132), all of which adopt one of two major paradigms. On the one hand, horizontal scrolling timeline partition-editors such as ELAN (2017) facilitate the annotation of multiple ‘tiers’ of simultaneous activities. On the other hand, vertical ‘lists of turns’ editors such as CLAN (Macwhinney, 1992) facilitate a digital, media-synced version of Jefferson’s representations of turn-by-turn talk. However, these tools and paradigms were primarily designed to support forms of coding and computational analysis in interaction research that have been anathema to EM/CA approaches (Schegloff 1993). Their assumptions about how video recordings are processed, analyzed and rendered as data may have significant but unexamined consequences for EM/CA research. This 2.5 hour workshop will reflect on the praxeology of video analysis by running a series of activities that involve sharing and discussing diverse EM/CA methods of working with video. Attendees are invited to bring a video they have worked up from ‘raw data’ to publication, which we will re-analyze live using methods drawn from traditions of life drawing and still life. A small development team will build a series of paper and software prototypes over the course of the workshop week, aiming to put participants’ ideas and suggestions into practice. Overall, the workshop aims to inform the ongoing development of software tools designed reflexively to explore, support, and question the ways we use video and software tools in EM/CA research.


ELAN (Version 5.0.0-beta) [Computer software]. (2017, April 18). Nijmegen: Max Planck Institute for Psycholinguistics. Retrieved from

Heath, C., Hindmarsh, J., & Luff, P. (2010). Video in qualitative research: analysing social interaction in everyday life. London: Sage Publications.

Hepburn, A., & Bolden, G. B. (2017). Transcribing for social research. London: Sage.

MacWhinney, B. (1992). The CHILDES project: Tools for analyzing talk. Child Language Teaching and Therapy, (2000).

Schegloff, E. A. (1993). Reflections on Quantification in the Study of Conversation. Research on Language & Social Interaction, 26(1), 99–128.

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I’m presenting with Dirk vom Lehn on a panel organized by two fantastic EM/CA scholars Richard Ogden and Leelo Keevalik on ‘non-lexical vocalizations’. We’re using some great video data we collected featuring novice dancers in a Swing Patrol ‘Dance in a Day’ workshop as part of the dance as interaction project.

CA studies of assessments as distinct, sequentially organized social actions (Pomerantz, 1984) have tended to define assessments for the purposes of data selection (Ogden, 2006, p. 1758) as “utterances that offer an evaluation of a referent with a clear valence” (Stivers & Rossano, 2010). However, this definition may exclude evaluative practices where the ‘valenced’ terms of assessment are more equivocal. It also obscures how the valences that mark out an utterance as an assessment are produced interactionally in the first place. This paper follows Goodwin & Goodwin’s (1992) proposal that assessment ‘segments’ (words like ‘good’ or ‘beautiful’), and assessment ‘signals’ (vocalizations like “mmm!” or “ugh!”) are organized into sequential ‘slots’ that render both ‘segments’ and ‘signals’ reflexively accountable as evaluative ‘assessment activities’. Data are drawn from recordings of a novice partner dance workshop at moments where teachers’ pro-forma terminal assessments marking the completion of a dance practice session co-occur with students’ evaluative assessment activities. Analysis shows how students use non-lexical vocalizations as evaluative assessments after imitating the bodily-vocal demonstrations (Keevalik, 2014) of the teachers and completing an unfamiliar dance move together. Extract 1 shows one example of these non-lexical vocalizations as dance partners Paul and Mary complete a new dance movement while the teachers call out rhythms and instructions.

Extract 1

1 Tch1: tri:ple and ⌈rock step (0.8) BRINGING I::n. a::n rock step
2 Tch2:             ⌊rock step tri:ple an tri:ple a::n ro̲c̲k step
3 Tch1: tri:ple (.) tri:ple.≈
4 Mary: ≈⌈So̲rry. <(I’m a) little AUa:⁎U:h⁎ ((Shifts arm down Paul’s shoulder))
5 Tch2:  ⌊(a::nd then sto:p?)
6 Paul: Ye:: sHheh a:̲h⌈- yeh. (.) ∙HEh UhUH ->
7 Mary:               ⌊it⌈'s li- Au̲h- uh. ((Re-does and emphsizes arm-shift))
8 Tch1:                  ⌊ROTATE P::̲↑ARTne::::r::s::.
9        (0.8)
10 Mary:  ⌈Eya̲a̲::: ((Makes a clawing gesture))
11 Paul:  ⌊The bh- the bi̲:cep clench (°>dy'a know wha' I mean<°)≈ ->
12 Mary: ≈↑Y e̲a̲h̲h̲.⌈ it's- it's b- hh((Re-does and emphasizes clawing gesture))
13 Paul:          ⌊HAH hah Ha::h °hah hah° ∙HHh Heh heh ∙hh
14 Tch1: SO: WITH YOUR NE̲W̲ P:̲A̲R̲TNE:⌈:r.
15 Paul:                           ⌊That's an odd way of descri:bing it.

The analysis suggests that non-lexical vocalizations provide a useful resource for evaluating the achievement of as-yet-unfamiliar joint actions and managing and calibrating subtle degrees and dimensions of individual and mutual accountability for troubles encountered in learning a new, unfamiliar partner dance movement.


  • Goodwin, C., & Goodwin, M. H. (1992). Context, activity and participation. In P. Auer & A. D. Luzio, P. Auer & A. D. Luzio (Eds.), The contextualization of language (pp. 77–100). John Benjamins.
  • Keevallik, L. (2014). Turn organization and bodily-vocal demonstrations. Journal of Pragmatics, 65, 103–120.
  • Ogden, R. (2006). Phonetics and social action in agreements and disagreements. Journal of Pragmatics, 38(10), 1752–1775.
  • Pomerantz, A. (1984). Agreeing and disagreeing with assessments: Some features of preferred/dispreferred turn shapes. In J. M. Atkinson & J. Heritage, (Eds.), Structures of social action: Studies in conversation analysis (pp. 57–102). Cambridge: Cambridge University Press.
  • Stivers, T., & Rossano, F. (2010). Mobilizing Response. Research on Language & Social Interaction, 43(1), 3–31.

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Mick Smith and I are organizing this panel on noticings at ICCA 2018. We’re really excited to have submissions from some amazing EM/CA scholars to help us explore this questions of action formation / ascription, embodiment, multiactivity, and reference across at least three languages.

Noticings as actions-in-conversation are a ubiquitous, versatile, but under-researched phenomenon (Keisanen, 2012). Schegloff (2007b, p. 218) suggests that noticings “put on offer a line of talk” that renders something optionally relevant for subsequent interaction, although Stivers & Rossano’s (2010) study of the diminished ‘response-relevance’ of noticings leads some analysts to question whether noticings function as social actions (Thompson, Fox, & Couper-Kuhlen, 2015, p. 141) formed from prospectively paired ‘action types’ (Levinson, 2013), or whether they are organised—as Schegloff (2007b, p. 219) suggests—as a generic retro-sequence pointing backwards to a prior ‘noticeable’. Alongside these debates, C. Goodwin & Goodwin (2012) focus on how noticings point “outside of talk”, drawing as-yet-unnoticed resources into embodied social action. Without pre-specifying any one analytic characterization, this panel brings together research explores the ambiguities of noticings as social actions alongside a range of mobile and embodied practices where describing (Sidnell & Barnes, 2009), referring (Hindmarsh & Heath, 2000), and categorizing may also be at issue (Schegloff, 2007a). Alongside empirical studies, contributors also address theoretical questions that arise from treating noticings as conversational devices. How are researchers’ noticings and participants’ noticings differently constitutive of interactional phenomena (Laurier, 2013)? Do noticings emerge reflexively as part of a particular interactional environment and work towards particular interactional ends (Schegloff, 2007a, p. 87 note 17), or are analytic invocations of ‘noticing’ in CA flawed descriptions that obscure more of the action than they clarify? Drawing together diverse approaches to noticings, this panel asks how understanding noticings as actions-in-conversation may open up new empirical and theoretical questions and challenges.


  • Goodwin, C., & Goodwin, M. H. (2012). Car talk: Integrating texts, bodies, and changing landscapes. Semiotica, 191(1/4), 257–286.
  • Hindmarsh, J., & Heath, C. (2000). Embodied reference: A study of deixis in workplace interaction. Journal of Pragmatics, 32(12), 1855–1878.
  • Keisanen, T. (2012). “Uh-oh, we were going there”: Environmentally occasioned noticings of trouble in in-car interaction. Semiotic, 191(1/4), 197–222.
  • Laurier, E. (2013). Noticing: Talk, gestures, movement and objects in video analysis. In R. Lee, N. Castree, R. Kitchin, V. Lawson, A. Paasi, C. Philo, … C. W. Withers (Eds.), The SAGE Handbook of Human Geography (2nd ed., Vol. 31, pp. 250–272). London: Sage.
  • Levinson, S. C. (2013). Action formation and ascription. In J. Sidnell & T. Stivers (Eds.), The Handbook of Conversation Analysis (pp. 101–130). Oxford: John Wiley & Sons.
  • Schegloff, E. A. (2007a). A tutorial on membership categorization. Journal of Pragmatics, 39(3), 462–482.
  • Schegloff, E. A. (2007b). Sequence organization in interaction: Volume 1: A primer in conversation analysis, Cambridge: Cambridge University Press.
  • Sidnell, J., & Barnes, R. (2009). Alternative, subsequent descriptions. In J. Sidnell, M. Hayashi, & G. Raymond (Eds.), Conversational repair and human understanding (pp. 322–342). Cambridge: Cambridge University Press.
  • Stivers, T., & Rossano, F. (2010). Mobilizing Response. Research on Language & Social Interaction, 43(1), 3–31.
  • Thompson, S. A., Fox, B. A., & Couper-Kuhlen, E. (2015). Grammar in everyday talk: Building responsive actions. Cambridge: Cambridge University Press.

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Here’s the abstract to an ICCA 2018 paper I’m working on with J.P. de Ruiter at the Human Interaction Lab at Tufts. The goal is to use computational linguistic methods (that often use the term ‘backchannel’) to see if all these responsive particles really belong in one big undifferentiated ‘bucket’.

Many studies of dialogue use the catch-all term ‘backchannel’ (Yngve ,1970) to refer to a wide range of utterances and behaviors as forms of listener-feedback in interaction. The use of this wide category ignores nearly half a century of research into the highly differentiated interactional functions of ‘continuers’ such as ‘uh huh’ or ‘wow’ (Schegloff, 1982, Goodwin, 1986), acknowledgement tokens such as ‘yeah’, ‘right’ or ‘okay’ (Jefferson, 1984; Beach, 1993) and change-of-state markers such as ‘oh’ or ‘nå’ (Heritage, 1984; Heinemann, 2017). These studies show how participants use responsive particles as fully-fledged, individuated, and distinctive words that do not belong in an undifferentiated functional class of ‘backchannels’ (Sorjonen, 2001). For this paper we use the Conversation Analytic British National Corpus (CABNC) (Albert, L. de Ruiter & J. P. de Ruiter, 2015) – a 4.2M word corpus featuring audio recordings of interaction from a wide variety of everyday settings that facilitates ‘crowdsourced’ incremental improvements and multi-annotator coding. We use Bayesian model comparison to evaluate the relative predictive performance of two competing models. In the first of these, all ‘backchannels’ imply the same amount of floor-yielding, while the second CA informed model assumes that different response tokens are more or less effective in ushering extended turns or sequences to a close. We argue that using large corpora together with statistical models can also identify candidate ‘deviant cases’, providing new angles and opportunities for ongoing detailed, inductive conversation analysis. We discuss the methodological implications of using “big data” with CA, and suggest key guidelines and common pitfalls for researchers using large corpora and statistical methods at the interface between CA and cognitive psychology (De Ruiter & Albert, 2017).

References (including references for the final talk – which has many more references than this abstract).

  • Albert, S., De Ruiter, L., & De Ruiter, J. P. (2015). The CABNC. Retrieved from 9/09/2017
  • Albert, S., & De Ruiter, J.P. (2018, in press), Ecological grounding in interaction research. Collabra: Psychology.
  • Beach, W. A. (1990). Searching for universal features of conversation. Research on Language &amp; Social Interaction, 24(1–4), 351–368.
  • Bolden, G. B. (2015). Transcribing as Research: ‘Manual’; Transcription and Conversation Analysis. Research on Language and Social Interaction, 48(3), 276–280.
  • de Ruiter, J. P., & Albert, S. (2017). An Appeal for a Methodological Fusion of Conversation Analysis and Experimental Psychology. Research on Language and Social Interaction, 50(1), 90–107.
  • Goodwin, C. (1986). Between and within: Alternative sequential treatments of continuers and assessments. Human Studies, 9(2), 205–217.
  • Greiffenhagen, C., Mair, M., & Sharrock, W. (2011). From Methodology to Methodography: A Study of Qualitative and Quantitative Reasoning in Practice. Methodological Innovations Online, 6(3), 93–107.
  • Hayashi, M., & Yoon, K. (2009). Negotiating boundaries in talk. Conversation Analysis: Comparative Perspectives, 27, 250.
  • Hepburn, A., & Bolden, G. B. (2017). Transcribing for social research. London: Sage.
  • Heritage, J. (1984). A change-of-state token and aspects of its sequential placement. In M. Atkinson & J. Heritage, M. Atkinson & J. Heritage (Eds.), Structures of social action: Studies in conversation analysis (pp. 299–345). Cambridge: Cambridge University Press.
  • Heritage, J. (1998). Oh-prefaced responses to inquiry. Language in Society, 27(3), 291–334.
  • Heritage, J. (2002). Oh-prefaced responses to assessments: A method of modifying agreement/disagreement. In C. E. Ford, B. A. Fox, & S. A. Thompson, C. E. Ford, B. A. Fox, & S. A. Thompson (Eds.), The Language of Turn and Sequence (pp. 1–28). New York: Oxford University Press.
  • Hoey, E. M., & Kendrick, K. H. (2017). Conversation Analysis. In A. M. B. de Groot & P.Hagoort, A. M. B. de Groot & P.Hagoort (Eds.), Research Methods in Psycholinguistics: A Practical Guide (pp. 151–173). Hoboken, NJ: WileyBlackwell.
  • Housley, W., Procter, R., Edwards, A., Burnap, P., Williams, M., Sloan, L., … Greenhill, A. (2014). Big and broad social data and the sociological imagination: A collaborative response. Big Data &amp; Society, 1(2).
  • Jefferson, G. (1981). On the Articulation of Topic in Conversation. Final Report. London: Social Science Research Council.
  • Jefferson, G. (1984). Notes on a systematic Deployment of the Acknowledgement tokens ’Yeah’ and ’Mmhm’. Papers in Linguistics, 17(2), 197–216.
  • Kendrick, K. H. (2017). Using Conversation Analysis in the Lab. Research on Language and Social Interaction , 1–11.
  • MacWhinney, B. (1992). The CHILDES project: Tools for analyzing talk. Child Language Teaching and Therapy, (2000).
  • Nishizaka, A. (2015). Facts and Normative Connections: Two Different Worldviews. Research on Language and Social Interaction, 48(1), 26–31.
  • Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606.
  • Ochs, E. (1979). Transcription as theory. In E. Ochs & B. B. Schieffelin, E. Ochs & B. B. Schieffelin (Eds.), Developmental pragmatics (pp. 43–72). New York: Academic Press.
  • Potter, J., & te Molder, H. (2005). Talking cognition: Mapping and making the terrain. In J. Potter & D. Edwards, J. Potter & D. Edwards (Eds.), Conversation and cognition (pp. 1–54).
  • Sacks, H. (1963). Sociological description. Berkeley Journal of Sociology, 1–16.
  • Schegloff, E. A. (1982). Discourse as an interactional achievement: Some uses of ?uh huh?and other things that come between sentences. In D. Tannen, D. Tannen (Ed.), Analyzing discourse: Text and talk (pp. 71–93). Georgetown University Press.
  • Schegloff, E. A. (2007). Sequence organization in interaction: Volume 1: A primer in conversation analysis. Cambridge: Cambridge University Press.
  • Steensig, J., & Heinemann, T. (2015). Opening Up Codings? Research on Language and Social Interaction, 48(1), 20–25.
  • Stivers, T. (2015). Coding Social Interaction: A Heretical Approach in Conversation Analysis? Research on Language and Social Interaction, 48(1), 1–19.
  • Rühlemann (2017). Integrating Corpus-Linguistic and Conversation-Analytic Transcription in XML: The Case of Backchannels and Overlap in Storytelling Interaction. Corpus Pragmatics, 1(3), 201–232.
  • Rühlemann, C., & Gee, M. (2018). Conversation Analysis and the XML method. Gesprächsforschung–Online-Zeitschrift Zur Verbalen Interaktion, 18.
  • Wittenburg, P., Brugman, H., Russel, A., Klassmann, A., & Sloetjes, H. (2006). ELAN: a professional framework for multimodality research. In 5th International Conference on Language Resources and Evaluation (LREC 2006) (pp. 1556–1559).
  • Yngve, V. (1970). On getting a word in edgewise. Chicago Linguistics Society, 6th Meeting, 566–579. Retrieved from

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