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.

1. 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

2. 2

3. 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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4. 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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5. motivation (3/3)
micro-serendipity on Twitter
 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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6. 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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7. methodology (1/4)
data collection
 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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8. methodology (2/4)
coding tweets
 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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9. methodology (3/4)
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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10. methodology (4/4)
‘serendipity’ noise
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11. 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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12. work- and
leisure-
related
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13. work-related
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14. leisure-
related
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15. findings: RQ1 (2/4)
serendipity thresholds & continuum
 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:
unplanned unanticipated eureka
everyday incidents moments in science
 serendipity is not a discrete concept
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16. serendipity thresholds
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17. 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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18. foreground serendipity (‘synchronicity’)
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19. 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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20. unexpectedness + value + preoccupation
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21. unexpectedness + value + preoccupation
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22. 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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23. 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
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24. 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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25. 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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26. 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
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27. 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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28. questions? comments?
Lennart Björneborn @connecto
Toine Bogers @toinebogers
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