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Presentation at iConference 2013, Feb. 13, 2013, Fort Worth, Texas. Fulltext paper available: http://hdl.handle.net/2142/36052 Bogers, T. & Björneborn, L. (2013). Micro-serendipity: Meaningful coincidences in everyday life shared on Twitter. Proceedings of iConference 2013, pp. 196-208.
Citation preview
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?
serendipity: finding interesting things in unplanned ways
important role in many scientific discoveries
also integral part in everyday information behavior
how we get new inspiration, ideas, insights in everyday life
the very way we learn many new things in life since infanthood
design for stimulating and supporting serendipity
search engines, recommender systems (e.g., music), micro-
blogging, …
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motivation (2/3)
needed: better understanding
different definitions focus on different aspects:
include active (foreground) interest?
relate to latent (background) interest alone?
needed: better understanding how people experience and
communicate serendipitous occurrences in everyday life
naturalistic studies of everyday serendipity
based on data generated by users themselves (Erdelez, 2004)
most previous studies based on data elicited from interviews
everyday serendipitous experiences of bloggers (Rubin et al., 2011)
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micro-serendipity: investigating contexts and attributes of
everyday serendipity as shared on Twitter
we use non-elicited, self-motivated user data from Twitter
we omit a preset definition of serendipity
understand what users themselves consider as serendipitous
experiences and how they actually describe these experiences
Twitter: window into everyday life of millions of users
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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crawled 30,000+ English-language tweets containing the
term ‘serendipity’ from Aug 2011–Feb 2012
used Topsy, social media search engine to access tweets
can search further back in time than Twitter
access to max. 1% of all tweets
no obvious crawling bias, so assumed to be representative
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methodology (1/4)
data collection
open coding approach to develop coding categories
on Feb 2012 tweets
category of interest: PERS (personal)
clearly describe personal insight or experience of a
serendipitous occurrence on the part of the tweeter
we tried to eliminate our pre-conceptions of what serendipity is
used context (included URLs and surrounding tweet stream)
to disambiguate
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methodology (2/4)
coding tweets
applied coding scheme to last three months of tweets
with the hashtag #serendipity (Dec 2011–Feb 2012)
open coding phase showed #serendipity more likely to contain
PERS tweets
inter-annotator agreement of 0.65
remaining differences resolved through discussion
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?
qualitative analysis of 160 tweets in PERS category
distinction between leisure- and work-related activities
141 tweets (88.1%) leisure-related
14 tweets (8.8%) work-related
1 tweet coded as both; 4 tweets too ambiguous to code
rich diversity in leisure-related activities connected to
serendipitous experiences
all kinds of digital and physical spaces
including media, shopping, sports and transportation
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work-related
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leisure-
related
unplanned
everyday incidents
unanticipated eureka
moments in science
different serendipity thresholds
when does a user find something unusual, unexpected, or surprising
enough to consider it as serendipity?
plain novelty or pleasant diversion may sometimes be enough
serendipity is a highly subjective phenomenon
serendipity continuum
different degrees of surprise:
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
background serendipity (‘traditional’ serendipity)
unexpectedly finding something meaningful related to a background
interest; changing a person’s focus and direction
foreground serendipity (‘synchronicity’)
unexpectedly finding something meaningful related to a foreground
interest/preoccupation; confirming a person’s focus and direction
in everyday experiences and in science (e.g., Makri & Blandford, 2012)
both types of serendipity deal with people experiencing
meaningful coincidences
people considering an occurrence as both meaningful and incidental
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foreground serendipity (‘synchronicity’)
findings: RQ1 (4/4)
key elements in serendipity
unexpectedness + insight + value (Makri & Blandford, 2012)
unexpectedness + value + preoccupation
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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unexpectedness + value + preoccupation
findings: RQ2
frequency of sharing serendipity
RQ 2 How often do people share serendipitous
experiences on Twitter?
160 PERS tweets from 146 different users
tweets from all users with >1 PERS tweets were identical
repetitions
extended this to the full 7-month, 30,000+ tweet crawl
only a handful users had more than one tweet about serendipity
not that common a (re-)occurrence on Twitter!
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?
two reasons for answering this question
general interest in how people describe serendipitous occurrences
can we train an automatic classifier to pick out PERS tweets?
focused on three ways of signaling serendipity
words
part-of-speech tags (e.g., noun, past tense verb, …)
hashtags (e.g., #serendipitous, #insight, …)
used log-likelihood to extract representative signals
measures how surprising the usage of a signal between two text
collections is 23
findings: RQ3 (2/3)
describing serendipity
words
PERS:
just, found, noticed, bumped, simultaneously, immediately, omg
non-PERS:
watching, serendipity, Kate, John, movie, chocolate, sundae
no conclusive identification of serendipity vocabulary
parts-of-speech
past tense verbs more often used in PERS tweets
present tense verbs more often used in non-PERS tweets
nouns more likely in non-PERS tweets
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findings: RQ3 (3/3)
describing serendipity
hashtags
hashtags most commonly co-occurring with #serendipity belong
to events: #nyc, #superbowl, #weezercruise, #saints
promising hashtags for future work:
#serendipitous, #synchronicity, #chance, #insight,
#randomness, #accident, #wtf, #lucky, #surprise
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
people experience this along a continuum with different thresholds
RQ 2: serendipity appears to be a rarely tweeted phenomenon
perhaps because it is uncommon or in fact too common?
longitudinal studies are necessary to confirm this though
RQ 3: no single signal singles out serendipitous occurrences
combination of different signals shows promise for automatic
classification
26
future work
actual word usage on Twitter may suggest terms for other
serendipity studies
developing an automatic serendipity classifier
include data from surrounding tweets in tweet stream
investigate how people describe matches between
environmental factors and foreground/background interests
include differences between physical and digital environments
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questions? comments?
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Lennart Björneborn @connecto
Toine Bogers @toinebogers