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  • In Search of UX Translators: Analyzing Researcher-Practitioner Interactions on Twitter

    Abstract Interest in the nature of HCI practice has increased in the past decade, particularly in relation to the role and existence of translators that may bridge the gap between research and UX practice. At present, there is insufficient research to appropriately define and identify the activities of translators, and we hope to provoke additional interest in this area by documenting the UX focused interactions on Twitter. In this work-in-progress, we identified and visualized interactions among a stratified set of UX practitioners and practitioner-academic hybrids on Twitter, analyzing their interactions to understand what relationships and roles may exist. We found few potential translators, and none from a primarily academic perspective. We identify implications and provocations from this visualization and Twitter analysis approach for future practice-led research.

    Author Keywords Translators; Twitter; practice-led research; social media; knowledge sharing; visualization.

    ACM Classification Keywords H.5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. DIS'17 Companion, June 10-14, 2017, Edinburgh, United Kingdom 2017 Copyright is held by the owner/author(s). ACM ISBN 978-1-4503-4991-8/17/06. http://dx.doi.org/10.1145/3064857.3079129

    Jason Brier Purdue University West Lafayette, IN 47907, USA brier1@purdue.edu Colin M. Gray Purdue University West Lafayette, IN 47907, USA gray42@purdue.edu Yubo Kou Purdue University West Lafayette, IN 47907, USA kou2@purdue.edu

  • Since the 1980s, the HCI research and practice communities have grown steadily apart, resulting in challenges in communication and dissemination of knowledge between academics and practitioners [1,6,8]. This disconnect has previously been studied by scholars in relation to practitioners use and understanding of methods [9], definitions of user experience design practice [2], and the types of knowledge that are needed to support HCI-oriented design activity [3]. However, there is still little understanding of how practitioners might productively relate to the research community, and how, according to Gray, Stolterman, and Siegel [1], practitioners might bubble-up aspects of their practice to advance HCI scholarship. We take a practice-led perspective, agreeing with other scholars that much HCI research fails to find its way to the practice community, and even when it does there are challenges with understanding, adoption, and usefulness [1,6,8,10,11].

    Norman [6] has proposed that this disconnect and lack of knowledge transfer could potentially be bridged by a new type of practitioner that plays the role of translator or translational developer. However, little research has been performed to document how a translator role may appear in the wild, or if such individuals exist. We also know little about what knowledge or methodologies practitioners rely upon to do their work, and many in the academic community have expressed frustrations about the low impact or lack of accurate translation their research has in industry settings [1,10].

    In interactions amongst experienced practitioners, there lies a wealth of knowledge about successful design methods and techniques that can be used to

    further both practice and academic research, but at present, there are no strong lines of communication back to the academic community that would give HCI researchers the opportunity to improve their work in hopes of further supporting practice [11]. While there is a history of small-scale practice-led research, in this work-in-progress, we use a broader sampling of UX practitioner interactions through Twitter to describe a broader view of relationships and thought leadership in the UX community. Previous studies have indicated that practitioners regularly interact on Twitter, and this platform has become an important medium that supports personal branding and impression management [5]. Further, this platform is commonly used to manage ones professional identity and engage in professional communication with colleagues [4]. The contribution of this work is an early step towards empirical identification of translators on Twitter, and a provocation for researchers to consider the thought leaders and modes of knowledge dissemination that have value in supporting contemporary UX practice.

    Method We identified a list of influential UX celebrities on Twitter, comprising stratified groups of practitioners and practitioner-academic hybrids who may potentially be taking on the role of translators. We took into consideration speaking engagements at practitioner and academic HCI/UX conferences, book publications, and related Twitter followers, among other identified influencers. In all, 35 Twitter accounts were selected (see sidebar), and these accounts became the seed nodes used to construct a network data visualization. For each account, we collected approximately 3200 tweets, including all metadata (n=98541; M=2815.45; SD=833.35). We also documented retweets and @reply

  • interactions between accounts. We identified all the tweets where non-seeded user accounts were involved in an interaction, and narrowed that set of secondary nodes to include those with a minimum of 50 mentions by any combination of seed nodes. This set of 645 nodes represent other potential thought leaders that

    may have been missed in our initial selection process. After collecting this data, we used the D3.js visualization library to generate a force-directed network diagram that visualized the interactions among the seeded nodes and seed or secondary nodes.

    Figure 1: Force-directed network diagram of Twitter interactions. The size of orange nodes (secondary) are weighted by frequency and the size of blue nodes are weighted by follower count.

    fabtweet

    jgrudinjofish

    wasbuxton

    kholtzblatt

    jkolko

    ebuie

    jcolman

    bobulate

    goldenkrishna

    jouleebokardo

    zeldman

    jbrewerlukew

    aarronjohnmaeda

    jmspool

    karenmcgrane

    skrug

    andybudd

    lauraklein

    rosenfeldmedia

    katerutter

    odannyboy

    peterme

    jnd1eradaptivepath

    riander

    janepyle

    chrisrisdon

    mralancooper

    boagworld

    jeffbigham

    fabtweet

    jgrudinjofish

    wasbuxton

    kholtzblatt

    jkolko

    ebuie

    jcolman

    bobulate

    goldenkrishna

    jouleebokardo

    zeldman

    jbrewerlukew

    aarronjohnmaeda

    jmspool

    karenmcgrane

    skrug

    andybudd

    lauraklein

    rosenfeldmedia

    katerutter

    odannyboy

    peterme

    jnd1eradaptivepath

    riander

    janepyle

    chrisrisdon

    mralancooper

    boagworld

    jeffbigham

    johnmaeda

    zeldman

    lukew

    rosenfeldmedia

    jmspool

    frank_chimero

    joulee

    adaptivepath

    andybudd

    aarron

    boagworld

    karenmcgrane

    bokardo

    jbrewer

    odannyboy

    bobulate

    jcolman

    peterme

    lauraklein

    fabtweet

    janepyle

    jkolko

    goldenkrishna

    katerutter

    chrisrisdon

    riander

    100K

    FOLLOWER COUNT

    POTE

    NTI

    AL

    TRA

    NSL

    ATO

    RS

    PRAC

    TITI

    ON

    ERS

    200K

    300K

    400K

    skrug

    jnd1er

    mralancooper

    wasbuxton

    jeffbigham

    ebuie

    jofish

    kholtzblatt

    jgrudinPRACTITIONER-ACADEMIC OR ACADEMIC

    SECONDARY NODE (@mention or retweet)

    PRACTITIONER

    Stratified Twitter Sample*

    Practitioners l johnmaeda, zeldman, lukew, jmspool, joule, andybudd, aarron, boagworld, karenmcgrane, bokardo, jbrewer, odannyboy, bobulate, jcolman, peterme, lauraklein, fabtweet, janepyle, jkolko, goldenkrishna, katerutter, chrisrisdon, riander

    Agencies l rosenfeldmedia, adaptivepath

    Practitioner-Academics (or published authors) l skrug, jnd1er, mralancooper, wasbuxton, ebuie, jofish, kholtzblatt, jgrudin

    Academics l jeffbigham

    *Usernames reverse-ordered by follower count and color-keyed to Figure 1. The Twitter accounts we sampled were intended by the respective account owners as a means of publicity, with no reasonable expectation of privacy.

  • Findings The degree centrality of the seed and secondary nodes in Figure 1 indicates that UX practitioners tended to dominate interactions in the sample, producing higher volume interactions that were more connected to other seed nodes (and thus, more central). In contrast, accounts that we initially identified as potential practitioner-academics or academics were located primarily along the periphery of the network diagram, showing less shared interaction and less frequent interactions. While we did not directly sample HCI researchers, with the exception of @jeffbigham there seem to be few academics involved in regular Twitter interactions with UX practitioners, and none could be considered central to the UX practice conversation on Twitter. Our goal is not to identify centrality as a metric for a researchers effectiveness, but rather as a point of methodological interest that requires further study. Among those most central in the network graph were well-known UX practitioners that regularly speak at practitioner conferences (e.g., @johnmaeda, @odannyboy, @jcolman). In addi