Micro-Serendipity: Meaningful Coincidences in Everyday Life Shared on Twitter

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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.

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Micro-Serendipity: Meaningful Coincidences in Everyday Life Shared on Twitter

  1. 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. 2
  3. 3. 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  l  l  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 l  search engines, recommender systems (e.g., music), microblogging, … 3
  4. 4. 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) 4
  5. 5. motivation (3/3) micro-serendipity on Twitter 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  l  understand what users themselves consider as serendipitous experiences and how they actually describe these experiences Twitter: window into everyday life of millions of users l  everyday experiences, interests, conversations, language use 5
  6. 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? 6
  7. 7. methodology (1/4) data collection 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 7
  8. 8. methodology (2/4) coding tweets 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 8
  9. 9. methodology (3/4) coding tweets l  applied coding scheme to last three months of tweets with the hashtag #serendipity (Dec 2011–Feb 2012) l  l  inter-annotator agreement of 0.65 l  l  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 9
  10. 10. methodology (4/4) ‘serendipity’ noise 10
  11. 11. 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  l  14 tweets (8.8%) work-related l  l  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 l  all kinds of digital and physical spaces l  including media, shopping, sports and transportation 11
  12. 12. work- and leisurerelated 12
  13. 13. work-related 13
  14. 14. leisurerelated 14
  15. 15. findings: RQ1 (2/4) serendipity thresholds & continuum l  different serendipity thresholds l  l  plain novelty or pleasant diversion may sometimes be enough l  l  when does a user find something unusual, unexpected, or surprising enough to consider it as serendipity? serendipity is a highly subjective phenomenon serendipity continuum l  different degrees of surprise: unplanned everyday incidents l  unanticipated eureka moments in science serendipity is not a discrete concept 15
  16. 16. serendipity thresholds 16
  17. 17. 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  l  in everyday experiences and in science (e.g., Makri & Blandford, 2012) both types of serendipity deal with people experiencing meaningful coincidences l  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 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 19
  20. 20. unexpectedness + value + preoccupation 20
  21. 21. 21
  22. 22. 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  l  tweets from all users with >1 PERS tweets were identical repetitions 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 22
  23. 23. 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  l  l  focused on three ways of signaling serendipity l  l  l  l  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 l  measures how surprising the usage of a signal between two text collections is 23
  24. 24. findings: RQ3 (2/3) describing serendipity l  words l  l  l  l  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 l  l  l  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 l  hashtags l  l  l  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. 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  l  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 l  combination of different signals shows promise for automatic classification 26
  27. 27. future work l  actual word usage on Twitter may suggest terms for other serendipity studies l  developing an automatic serendipity classifier l  l  include data from surrounding tweets in tweet stream investigate how people describe matches between environmental factors and foreground/background interests l  include differences between physical and digital environments 27
  28. 28. questions? comments? Lennart Björneborn @connecto Toine Bogers @toinebogers 28
  29. 29. extra
  30. 30. 31
  31. 31. findings / RQ1: experiencing serendipity serendipity context: leisure vs. work 32
  32. 32. findings / RQ3: describing serendipity terms signaling serendipity 33
  33. 33. 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  l  when our “chance encounters with information, objects, or people [...] lead to fortuitous outcomes” (Rubin et al. 2011) technologies for stimulating and supporting serendipity l  search engines, music recommender systems, micro-blogging, etc. 34
  34. 34. 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 35
  35. 35. ! #serendipity 36
  36. 36. 37

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