Micro-serendipity: Meaningful coincidences in everyday life shared on Twitter


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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.

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Micro-serendipity: Meaningful coincidences in everyday life shared on Twitter

  1. 1. Micro-serendipity:Meaningful Coincidencesin Everyday Life Shared on TwitteriConference 2013, Fort Worth, TXToine Bogers & Lennart BjörnebornRoyal School of Library and Information Science, Copenhagen
  2. 2. 2
  3. 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, … 3
  4. 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) 4
  5. 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 5
  6. 6. research questionsRQ 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? 6
  7. 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 7
  8. 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 8
  9. 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 9
  10. 10. methodology (4/4)‘serendipity’ noise 10
  11. 11. findings: RQ1 (1/4)serendipity context: leisure vs. workRQ 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 11
  12. 12. work- and leisure- related 12
  13. 13. work-related 13
  14. 14. leisure- related 14
  15. 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 15
  16. 16. serendipity thresholds 16
  17. 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 17
  18. 18. foreground serendipity (‘synchronicity’) 18
  19. 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 19
  20. 20. unexpectedness + value + preoccupation 20
  21. 21. unexpectedness + value + preoccupation 21
  22. 22. findings: RQ2frequency of sharing serendipityRQ 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 22
  23. 23. findings: RQ3 (1/3)describing serendipityRQ 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
  24. 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 24
  25. 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 25
  26. 26. conclusionsRQ 1: no single type of serendipity  people experience this along a continuum with different thresholdsRQ 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 thoughRQ 3: no single signal singles out serendipitous occurrences  combination of different signals shows promise for automatic classification 26
  27. 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 27
  28. 28. questions? comments? Lennart Björneborn @connecto Toine Bogers @toinebogers 28