Diversity-Enhancing Professional Social Matching

Jukka Huhtamäki

Postdoctoral Researcher

Tampere University

 

May 9, 2018 // University of Pittsburgh

Goals: social matching in knowledge work

  1. Enable unexpected social encounters in professional life
  2. ... without amplifying the emergence of echo chambers (homophily, triadic closure)
  3. ... by helping users avoid human bias in decision-making and identifying optimal similarity-diversity between matched people

Design requirements: context-sensitivity, systemic perspective, user-system cooperation, proactive persuation

(Olsson et al., CACM manuscript)

Our approach

  • Big Social Data (Olshannikova et al., 2017) on people, their networks and interactions as the fuel for the digital artefacts

  • Mapping existing social networks and identifying new potential connections between existing clusters

  • Action Design Research (Sein et al. 2011)

  • User experiments & field studies

  • Bridging the methodological gap
    between disciplines!

Theoretical background

Social structure of organizations

Social structure core concepts

Social structure in knowledge work

Experiment

  • Bibliographic data from dblp: 3M articles (title, authors, venue)
  • Feature vectors for each author: words in article titles (bigrams, TF-IDF)
  • Similarity is calculated between all pairs of actors (cosine similarity)
  • Recommendations: Most similar, somewhat similar, not similar

Experiment #1 (submitted to CSCW)

Echo chamber hypothesis:

  1. Actors prefer to connect with similar actors (homophily)
  2. New connections are formed between second-tier connections or friends-of-friends (triadic closure)

 

  1. Is the simplified similarity-based recommendation strategy able to produce new potential connections for actors?
  2. Does the similarity between actors indeed predict the perceived relevance of a connection?

Experiment #2

Under development

Experiment #2 (under development)

  • Breaking echo chambers:
    1. Using a network view of actors around an institution
    2. Social network provides context for recommendations

 

  1. Is the full network view valuable to users in exploring the social space?
  2. Do the users prefer to connect with actors in their second-tier network?
  3. Are the users willing to move beyond second-tier connections?

Experiment #2 details

  1. Twitter followers of the three universities in Tampere to be merged into Tampere University
  2. 300 most recent tweets from each follower
  3. Mention-based social network
  4. Topic modeling  (LDA)-based interest vectors
  5. Recommendation strategies under development and to be discussed
  6. User experiment to be run in the next few months

Toward systemic serendipity

Serendipitous social encounters

Title

  • Content

(McCay-Peet & Wells, 2017)

We adapt and extend the McCay-Peet & Toms (2015) serendipity model to social serendipity (submitted to CSCW 2018)

Outlook

Where should we go from here?

Social recommender systems

(Tsai & Brusilovsky, 2018)

Concluding remarks

  • We take a social network-centric viewpoint to social matching and seek to increase diversity by identifying potential weak ties into organizations 
  • We subscribe to Tsai and Brusilovsky (2018) in that instead of relevance-first approach, we should develop ways to increase transparency and user control in social matching    
  • However, nudging-based systems will also be developed and we should be able to inform their design 
  • Key short term objective is to investigate the perceived value of full social network-approach in identifying and recommending connections

Thank you!

Let the discussion continue:

 

medium.com/matching-people

#matchingpeople

Diversity-Enhancing Professional Social Matching

By Jukka Huhtamäki

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Diversity-Enhancing Professional Social Matching

Presentation at University of Pittsburgh in May 2018

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