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Micro-serendipity: Meaningful Coincidences in Everyday Life Shared on Twitter iConference 2013, Fort Worth, TX
Toine Bogers & Lennart Björneborn Royal School of Library and Information Science, Copenhagen
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motivation (1/3)
why is serendipity interesting? l serendipity: finding interesting things in unplanned ways
l important role in many scientific discoveries
l also integral part in everyday information behavior l how we get new impressions, ideas, insights in everyday life
l the very way we learn many new things in life since infanthood
l design for stimulating and supporting serendipity
l search engines, recommender systems (e.g., music), micro-blogging, …
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motivation (2/3)
needed: better understanding l different definitions focus on different aspects:
l include active (foreground) interest?
l relate to latent (background) interest alone?
l better understanding of how people experience and communicate serendipitous occurrences in everyday life
l naturalistic studies of everyday serendipity l based on data generated by users themselves (Erdelez, 2004)
l most previous studies based on data elicited from interviews
l everyday serendipitous experiences of bloggers (Rubin et al., 2011)
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l micro-serendipity: investigating contexts and attributes of everyday serendipity as shared on Twitter
l we use non-elicited, self-motivated user data from Twitter
l we omit a preset definition of serendipity
l understand what users themselves consider as serendipitous experiences and how they actually describe these experiences
l Twitter: window into everyday life of millions of users l everyday experiences, interests, conversations, language use
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motivation (3/3)
micro-serendipity on Twitter
research questions RQ 1 What types of serendipity do Twitter users
experience?
RQ 2 How often do people share serendipitous experiences on Twitter?
RQ 3 What terminology do people use on Twitter to describe their serendipitous experiences?
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l crawled 30,000+ English-language tweets containing the term ‘serendipity’ from Aug 2011–Feb 2012
l used Topsy, social media search engine to access tweets l can search further back in time than Twitter
l access to max. 1% of all tweets
l no obvious crawling bias, so assumed to be representative
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methodology (1/4)
data collection
l open coding approach to develop coding categories on Feb 2012 tweets
l category of interest: PERS (personal) l clearly describe personal insight or experience of a
serendipitous occurrence on the part of the tweeter
l we tried to eliminate our pre-conceptions of what serendipity is
l used context (included URLs and surrounding tweet stream) to disambiguate
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methodology (2/4)
coding tweets
l applied coding scheme to last three months of tweets with the hashtag #serendipity (Dec 2011–Feb 2012) l open coding phase showed #serendipity more likely to contain
PERS tweets
l inter-annotator agreement of 0.65
l remaining differences resolved through discussion
l coded 1073 tweets with 14.9% (N=160) in PERS category
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methodology (3/4)
coding tweets
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methodology (4/4)
‘serendipity’ noise
findings: RQ1 (1/4) serendipity context: leisure vs. work
RQ 1 What types of serendipity do users experience?
l qualitative analysis of 160 tweets in PERS category
l distinction between leisure- and work-related activities l 141 tweets (88.1%) leisure-related
l 14 tweets (8.8%) work-related
l 1 tweet coded as both; 4 tweets too ambiguous to code
l rich diversity in leisure-related activities connected to serendipitous experiences l all kinds of digital and physical spaces
l including media, shopping, sports and transportation 11
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work- and leisure- related
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work-related
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leisure- related
unplanned everyday incidents
unanticipated eureka moments in science
l different serendipity thresholds l when does a user find something unusual, unexpected, or surprising
enough to consider it as serendipity?
l plain novelty or pleasant diversion may sometimes be enough
l serendipity is a highly subjective phenomenon
l serendipity continuum l different degrees of surprise:
l serendipity is not a discrete concept
findings: RQ1 (2/4) serendipity thresholds & continuum
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serendipity thresholds
findings: RQ1 (3/4) background + foreground serendipity
l background serendipity (‘traditional’ serendipity) l unexpectedly finding something meaningful related to a background
interest; changing a person’s focus and direction
l foreground serendipity (‘synchronicity’) l unexpectedly finding something meaningful related to a foreground
interest/preoccupation; confirming a person’s focus and direction l in everyday experiences and in science (e.g., Makri & Blandford, 2012)
l both types of serendipity deal with people experiencing meaningful coincidences l people considering an occurrence as both meaningful and incidental
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foreground serendipity (‘synchronicity’)
findings: RQ1 (4/4) key elements in serendipity l unexpectedness + insight + value (Makri & Blandford, 2012)
l unexpectedness + value + preoccupation
l some degree of insight always present in order to consider an occurrence as both unexpected/incidental and valuable/meaningful; – i.e., considering the occurrence as a meaningful coincidence
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unexpectedness + value + preoccupation
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findings: RQ2 frequency of sharing serendipity
RQ 2 How often do people share serendipitous experiences on Twitter?
l 160 PERS tweets from 146 different users
l tweets from all users with >1 PERS tweets were identical repetitions
l extended this to the full 7-month, 30,000+ tweet crawl l only a handful users had more than one tweet about serendipity
l not that common a (re-)occurrence on Twitter!
l we only focused on only one way of describing serendipity
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findings: RQ3 (1/3) describing serendipity
RQ 3 What terminology do people use on Twitter to describe their serendipitous experiences?
l two reasons for answering this question l general interest in how people describe serendipitous occurrences l can we train an automatic classifier to pick out PERS tweets?
l focused on three ways of signaling serendipity l words l part-of-speech tags (e.g., noun, past tense verb, …) l hashtags (e.g., #serendipitous, #insight, …)
l used log-likelihood to extract representative signals l measures how surprising the usage of a signal between two text
collections is 23
findings: RQ3 (2/3) describing serendipity
l words l PERS:
just, found, noticed, bumped, simultaneously, immediately, omg l non-PERS:
watching, serendipity, Kate, John, movie, chocolate, sundae l no conclusive identification of serendipity vocabulary
l parts-of-speech l past tense verbs more often used in PERS tweets l present tense verbs more often used in non-PERS tweets l nouns more likely in non-PERS tweets
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findings: RQ3 (3/3) describing serendipity
l hashtags l hashtags most commonly co-occurring with #serendipity belong
to events: #nyc, #superbowl, #weezercruise, #saints
l promising hashtags for future work: #serendipitous, #synchronicity, #chance, #insight, #randomness, #accident, #wtf, #lucky, #surprise
l combination of different signals seems to show promise in automatic classification of PERS tweets
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conclusions
RQ 1: no single type of serendipity l people experience this along a continuum with different thresholds
RQ 2: serendipity appears to be a rarely tweeted phenomenon l perhaps because it is uncommon or in fact too common? l longitudinal studies are necessary to confirm this though
RQ 3: no single signal singles out serendipitous occurrences
l combination of different signals shows promise for automatic classification
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future work
l actual word usage on Twitter may suggest terms for other serendipity studies
l developing an automatic serendipity classifier l include data from surrounding tweets in tweet stream
l investigate how people describe matches between environmental factors and foreground/background interests l include differences between physical and digital environments
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questions? comments?
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Lennart Björneborn @connecto
Toine Bogers @toinebogers
extra
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findings / RQ1: experiencing serendipity serendipity context: leisure vs. work
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findings / RQ3: describing serendipity terms signaling serendipity
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motivation: 1(4) why is serendipity interesting? l serendipity: the accidental yet beneficial discovery
of something one was not looking for directly
l important role in many scientific discoveries l also integral part in everyday information behavior
l when our “chance encounters with information, objects, or people [...] lead to fortuitous outcomes” (Rubin et al. 2011)
l technologies for stimulating and supporting serendipity l search engines, music recommender systems, micro-blogging, etc.
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motivation: 2(4) tricky phenomenon & concept l studying the phenomenon and using the concept
in information science are not without difficulties
l different definitions focus on different aspects l include active (foreground) information seeking task?
l or relate to background interest alone?
l different weights to personal and environmental factors
l different thresholds for calling something serendipitous
l used synonymously with synchronicity, diversity, novelty
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! #serendipity
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