In this paper we present work on micro-serendipity: investigating everyday contexts, conditions, and attributes of serendipity as shared on Twitter. In contrast to related work, we deliberately omit a preset definition of serendipity to allow for the inclusion of micro- occurrences of what people themselves consider as meaningful coincidences in everyday life. We find that different people have different thresholds for what they consider serendipitous, revealing a serendipity continuum. We propose a distinction between background serendipity (or ‘traditional’ serendipity) and foreground serendipity (or ‘synchronicity’, unexpectedly finding something meaningful related to foreground interests). Our study confirms the presence of three key serendipity elements of unexpectedness, insight and value, and suggests a fourth element, preoccupation (foreground problem/interest), which covers synchronicity. Finally, we find that a combination of features based on word usage, POS categories, and hashtag usage show promise in automatically identifying tweets about serendipitous occurrences.

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

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3. motivation (1/3)
why is serendipity interesting?
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serendipity: finding interesting things in unplanned ways
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important role in many scientific discoveries
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also integral part in everyday information behavior
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how we get new impressions, ideas, insights in everyday life
the very way we learn many new things in life since infanthood
design for stimulating and supporting serendipity
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search engines, recommender systems (e.g., music), microblogging, …
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4. motivation (2/3)
needed: better understanding
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different definitions focus on different aspects:
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include active (foreground) interest?
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relate to latent (background) interest alone?
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better understanding of how people experience and
communicate serendipitous occurrences in everyday life
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naturalistic studies of everyday serendipity
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based on data generated by users themselves (Erdelez, 2004)
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most previous studies based on data elicited from interviews
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everyday serendipitous experiences of bloggers (Rubin et al., 2011)
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5. motivation (3/3)
micro-serendipity on Twitter
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micro-serendipity: investigating contexts and attributes of
everyday serendipity as shared on Twitter
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we use non-elicited, self-motivated user data from Twitter
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we omit a preset definition of serendipity
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understand what users themselves consider as serendipitous
experiences and how they actually describe these experiences
Twitter: window into everyday life of millions of users
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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
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crawled 30,000+ English-language tweets containing the
term ‘serendipity’ from Aug 2011–Feb 2012
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used Topsy, social media search engine to access tweets
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can search further back in time than Twitter
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access to max. 1% of all tweets
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no obvious crawling bias, so assumed to be representative
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8. methodology (2/4)
coding tweets
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open coding approach to develop coding categories
on Feb 2012 tweets
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category of interest: PERS (personal)
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clearly describe personal insight or experience of a
serendipitous occurrence on the part of the tweeter
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we tried to eliminate our pre-conceptions of what serendipity is
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used context (included URLs and surrounding tweet stream)
to disambiguate
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9. methodology (3/4)
coding tweets
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applied coding scheme to last three months of tweets
with the hashtag #serendipity (Dec 2011–Feb 2012)
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inter-annotator agreement of 0.65
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open coding phase showed #serendipity more likely to contain
PERS tweets
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?
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qualitative analysis of 160 tweets in PERS category
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distinction between leisure- and work-related activities
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14 tweets (8.8%) work-related
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141 tweets (88.1%) leisure-related
1 tweet coded as both; 4 tweets too ambiguous to code
rich diversity in leisure-related activities connected to
serendipitous experiences
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all kinds of digital and physical spaces
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including media, shopping, sports and transportation
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12. work- and
leisurerelated
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13. work-related
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14. leisurerelated
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15. findings: RQ1 (2/4)
serendipity thresholds & continuum
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different serendipity thresholds
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plain novelty or pleasant diversion may sometimes be enough
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when does a user find something unusual, unexpected, or surprising
enough to consider it as serendipity?
serendipity is a highly subjective phenomenon
serendipity continuum
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different degrees of surprise:
unplanned
everyday incidents
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unanticipated eureka
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
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background serendipity (‘traditional’ serendipity)
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unexpectedly finding something meaningful related to a background
interest; changing a person’s focus and direction
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foreground serendipity (‘synchronicity’)
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unexpectedly finding something meaningful related to a foreground
interest/preoccupation; confirming a person’s focus and direction
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in everyday experiences and in science (e.g., Makri & Blandford, 2012)
both types of serendipity deal with people experiencing
meaningful coincidences
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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
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unexpectedness + insight + value (Makri & Blandford, 2012)
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unexpectedness + value + preoccupation
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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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22. findings: RQ2
frequency of sharing serendipity
RQ 2 How often do people share serendipitous
experiences on Twitter?
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160 PERS tweets from 146 different users
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tweets from all users with >1 PERS tweets were identical
repetitions
extended this to the full 7-month, 30,000+ tweet crawl
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only a handful users had more than one tweet about serendipity
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not that common a (re-)occurrence on Twitter!
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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?
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two reasons for answering this question
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focused on three ways of signaling serendipity
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general interest in how people describe serendipitous occurrences
can we train an automatic classifier to pick out PERS tweets?
words
part-of-speech tags (e.g., noun, past tense verb, …)
hashtags (e.g., #serendipitous, #insight, …)
used log-likelihood to extract representative signals
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measures how surprising the usage of a signal between two text
collections is
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24. findings: RQ3 (2/3)
describing serendipity
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words
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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
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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
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hashtags
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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
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people experience this along a continuum with different thresholds
RQ 2: serendipity appears to be a rarely tweeted phenomenon
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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
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combination of different signals shows promise for automatic
classification
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27. future work
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actual word usage on Twitter may suggest terms for other
serendipity studies
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developing an automatic serendipity classifier
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include data from surrounding tweets in tweet stream
investigate how people describe matches between
environmental factors and foreground/background interests
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include differences between physical and digital environments
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28. questions? comments?
Lennart Björneborn @connecto
Toine Bogers @toinebogers
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30. extra

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32. findings / RQ1: experiencing serendipity
serendipity context: leisure vs. work
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33. findings / RQ3: describing serendipity
terms signaling serendipity
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34. motivation: 1(4)
why is serendipity interesting?
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serendipity: the accidental yet beneficial discovery
of something one was not looking for directly
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important role in many scientific discoveries
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also integral part in everyday information behavior
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when our “chance encounters with information, objects, or people
[...] lead to fortuitous outcomes” (Rubin et al. 2011)
technologies for stimulating and supporting serendipity
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search engines, music recommender systems, micro-blogging, etc.
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35. motivation: 2(4)
tricky phenomenon & concept
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studying the phenomenon and using the concept
in information science are not without difficulties
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different definitions focus on different aspects
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include active (foreground) information seeking task?
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or relate to background interest alone?
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different weights to personal and environmental factors
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different thresholds for calling something serendipitous
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used synonymously with synchronicity, diversity, novelty
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36. ! #serendipity
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