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Predicting tie strength with ego network structures

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Predicting Tie Strength With Ego Network Structures
Simon Stolz*, Christian Schlereth
Public slides, available via slidesh...

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Chair of Digital Marketing
© WHU – Otto Beisheim School of Management
2November 2020
Introduction
Identification of percei...

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Chair of Digital Marketing
© WHU – Otto Beisheim School of Management
3November 2020
The Two Approaches to Tie Strength
Ti...

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Predicting tie strength with ego network structures

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Stolz, Simon / Schlereth, Christian (2021): "Predicting Tie Strength with Ego Network Structures", Journal of Interactive Marketing, 54(May): 40-52.

Not all social media “friends” are close friends but distinguishing them from mere acquaintances is an important task in marketing. The notion of a close friend is reflected in the metric tie strength, but the true tie strength is often unobserved in online social networks. With this research, we propose an approach that predicts real-world tie strength via online da-ta measures of similarity, interaction, and network data. At its core, we assess ego network structures to predict tie strength, i.e., all first-degree connections and the interlinkage among them. Ego networks are easier to obtain than full networks, and researchers can process them more efficiently. We explain why bridging ego network positions could be associated with real-world tie strength and demonstrate the high discriminatory power of related network measures. In combination with similarity and interaction measures, the precision of identifying real-world strong ties is 45%. Finally, we empirically highlight the practical relevance of this finding by demonstrating that people react stronger to suggestions of a close friend com-pared to an acquaintance in a social advertisement experiment.

Stolz, Simon / Schlereth, Christian (2021): "Predicting Tie Strength with Ego Network Structures", Journal of Interactive Marketing, 54(May): 40-52.

Not all social media “friends” are close friends but distinguishing them from mere acquaintances is an important task in marketing. The notion of a close friend is reflected in the metric tie strength, but the true tie strength is often unobserved in online social networks. With this research, we propose an approach that predicts real-world tie strength via online da-ta measures of similarity, interaction, and network data. At its core, we assess ego network structures to predict tie strength, i.e., all first-degree connections and the interlinkage among them. Ego networks are easier to obtain than full networks, and researchers can process them more efficiently. We explain why bridging ego network positions could be associated with real-world tie strength and demonstrate the high discriminatory power of related network measures. In combination with similarity and interaction measures, the precision of identifying real-world strong ties is 45%. Finally, we empirically highlight the practical relevance of this finding by demonstrating that people react stronger to suggestions of a close friend com-pared to an acquaintance in a social advertisement experiment.

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Predicting tie strength with ego network structures

  1. 1. Predicting Tie Strength With Ego Network Structures Simon Stolz*, Christian Schlereth Public slides, available via slideshare.com Forthcoming at Journal of Interactive Marketing (* corresponding author)
  2. 2. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 2November 2020 Introduction Identification of perceived strong ties Images from: Author Facebook page, nounproject.com Online social networks host large and steadily growing lists of friends and acquaintances…. … but who are the few closest ones?
  3. 3. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 3November 2020 The Two Approaches to Tie Strength Tie strength assessments are important - but different approaches exist Social Advertisements Word of Mouth (Offline)Viral Marketing (eWOM) E.g., Bakshy et al. (2012) E.g., Hayes, King, Ramirez (2016), Aral and Walker (2014) E.g., Brown and Reingnen (1987), Chu and Kim (2011) “Revealed Preference” Measures ≠ Perceived Tie Strength See Bapna et al. (2017), Wiese et al. (2015) (Similarity, Interactions, and Networks) Online Offline This question matters! Various papers show that tie strength is a fundamental metric in marketing Two distinct views have emerged: Online studies use “revealed preference” measures, whereas offline studies were able to ask for “perceptions” Prior “revealed preference” measures have limitations and are different to the actual perceived tie strength
  4. 4. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 4November 2020 Tie Strength Prediction Models A high-level overview of how “revealed preference” predictors Paper (Respondents) Ties Predictors Similarity Interaction Network Common contacts Full network measures Ego network bridging positions Our paper (41) 18,541 ✔ ✔ ✔ - ✔ Rotabi et al. (2017) (-) Undisclosed - - ✔ ✔ - Backstrom and Kleinberg (2014) (-) ~ 379m - ✔ ✔ - ✔ Jones et al. (2013) (789) 1,587 ✔ ✔ - - - Arnaboldi, Guazzini and Passarella (2013) (30) 7,103 ✔ ✔ - - - Gilbert and Karahalios (2009) (35) 2,184 ✔ ✔ ✔ - - Kahanda and Neville (2009) (-) 8,766 ✔ ✔ ✔ ✔ - These papers all seek to predict the individual perceptions of tie strength and build on predictors from the three pillars: • Similarity • Interactions • Network i
  5. 5. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 5November 2020 Approach Social network analysis and the ego network perspective While social network analysis (SNA) commonly look at full, sociocentric, or sampled networks, we propose to change the perspective and look into the microcosm of “ego networks” The ego network contains all first-degree friends of a person (the ego). For simplicity let’s look at the following synthetic network that contains a family and university friends:
  6. 6. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 6November 2020 Motivation of Structural Embeddedness Degree in ego networks Degree Centrality (hereinafter Degree) reflects, for example for node C it simply reflects. Alter D has 3 common contacts with ego, hence the degree of alter C is 3 (when excluding ego) Already Granovetter (1973) argues that strong ties spend large amounts of time with each other. Hence, if two friends spend a lot of time together, they will also have the chance to meet another person and be connected.
  7. 7. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 7November 2020 Implemented in Motivation of Bridging Positions Betweenness and dispersion Betweenness Centrality (hereinafter Betweenness) reflects the probability of a node to lie on one of the shortest paths in the network Alter D lies on 12 of the shortest paths among first degree contacts of ego, hence the betweenness of alter C is 12 (when excluding ego)* (* = Note that an easy way to see this is that 4 nodes are on the left of D, and 3 nodes are on the right: 3⋅4 = 12) Dispersion (Backstrom & Kleinberg, 2014) reflects if mutual friends of ego and one alter are not well connected. Not all of the mutual friends of ego and alter D know each, C – Z and B – Z are unconnected and also have no other neighbors in common, therefore D has a dispersion of 2. Maintaining relationships requires investments (Lin, 1999). While members within social circles often already know each other, events like birthday parties - or even following an invitation that coincidentally involves multiple social circles are signs of such an investment into relationships. University friend D is special, because the friend is the only one to know a family member. i
  8. 8. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 8November 2020 Empirical Approach Facebook user survey and data extraction Anonymized research data is hosted on Mendeley Data https://data.mendeley.com/datasets/hr9tjzj72v/ i Who of the contacts in your Facebook profile would you consider as very close to you? Netvizz (Rieder, 2013) is an open-source Facebook application that extracts Facebook networks and basic user information for scientific use. Through the indirect data collection we can identify n = 18,541 individual data points (dyads) through the 41 responses.
  9. 9. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 9November 2020 Validating the Ego Perspective All network based measures have superior performance in ego networks Following our line of reasoning, merging networks should not benefit the tie strength prediction task to test this empirically we compare sampled and ego perspectives via ROC curves A very good explanation of ROC curves is Fawcett (2004) in Machine Learning All network-based measures perform best in the ego network perspective. Particularly betweenness achieves a very high overall ROC-AUC value i
  10. 10. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 10November 2020 We formulate a prediction algorithm via logistic regression (also evaluate random forest and gradient boosting with similar or inferior results) Predictive Approach Utilizing 5-fold cross-validation we test the predictive ability in combined models and predict who are the closest friends via 5 randomly selected hold-out sets. Insight ROC curves and precision scores (with varying thresholds) agree that ego network measures (especially bridging positions) have high predictive power to identify the rare closest friends.
  11. 11. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 11November 2020 Practical demonstration: ability to identify the few rare positives via precision score All Combined (Threshold = 258) Actual Prediction No Strong Tie Strong Tie No Strong Tie 18,140 143 Strong Tie 143 115 Precision: 45% = 115 / (115 + 143) Precision Scores Sensitivity Analysis
  12. 12. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 12November 2020 Implications Our findings have implications for data privacy, peer influence research, and other networks 2 Data Privacy Even when users do not knowingly disclose who their closest friends are, platform providers can predict this information through ego network data. While prior research has emphasized the ability to predict personality traits via Facebook Likes (e.g., Youyou 2015), our research points to a similar application: The ability to infer closest friends (i.e., strong ties) from networks. 1 Peer Influence Research Previously revealed preference studies have predominantly used interactions to explain peer influence through tie strength. To the best of our knowledge, bridging positions in ego networks have not yet been used to explain peer influence, but have strong potential to do so. We encourage researchers to integrate ego network bridging positions into their portfolio of revealed preference measures for tie strength. Extension to Other Networks Beyond the classical online social networks, like Facebook, LinkedIn and Twitter, various on- and offline services host similar network data that can benefit substantially from knowing which of the users are closest to each other. For example long-term call records, phone directories, and apps that integrate networking functionality are likely to host similar structures in which social circles are visible. 3
  13. 13. Chair of Digital Marketing © WHU – Otto Beisheim School of Management 13November 2020 Research Data on Mendeley Data Get in Touch simon.stolz@whu.edu Research Data on Mendeley Data Stolz, Simon; Schlereth, Christian (2020), “Predicting Tie Strength with Ego Network Structures”, Mendeley Data, https://data.mendeley.com/datasets/hr9tjzj72v/ whu.edu/digital@SimStolz on Twitter i

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