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Camera Brand Congruence and Camera Model Propagation in the Flickr Social Graph

Published: 01 October 2011 Publication History

Abstract

Given that my friends on Flickr use cameras of brand X, am I more likely to also use a camera of brand X? Given that one of these friends changes her brand, am I likely to do the same? Do new camera models pop up uniformly in the friendship graph? Or do early adopters then “convert” their friends? Which factors influence the conversion probability of a user? These are the kind of questions addressed in this work. Direct applications involve personalized advertising in social networks.
For our study, we crawled a complete connected component of the Flickr friendship graph with a total of 67M edges and 3.9M users. 1.2M of these users had at least one public photograph with valid model metadata, which allowed us to assign camera brands and models to users and time slots. Similarly, we used, where provided in a user’s profile, information about a user’s geographic location and the groups joined on Flickr.
Concerning brand congruence, our main findings are the following. First, a pair of friends on Flickr has a higher probability of being congruent, that is, using the same brand, compared to two random users (27% vs. 19%). Second, the degree of congruence goes up for pairs of friends (i) in the same country (29%), (ii) who both only have very few friends (30%), and (iii) with a very high cliqueness (38%). Third, given that a user changes her camera model between March-May 2007 and March-May 2008, high cliqueness friends are more likely than random users to do the same (54% vs. 48%). Fourth, users using high-end cameras are far more loyal to their brand than users using point-and-shoot cameras, with a probability of staying with the same brand of 60% vs 33%, given that a new camera is bought. Fifth, these “expert” users’ brand congruence reaches 66% for high cliqueness friends. All these differences are statistically significant at 1%.
As for the propagation of new models in the friendship graph, we observe the following. First, the growth of connected components of users converted to a particular, new camera model differs distinctly from random growth. Second, the decline of dissemination of a particular model is close to random decline. This illustrates that users influence their friends to change to a particular new model, rather than from a particular old model. Third, having many converted friends increases the probability of the user to convert herself. Here differences between friends from the same or from different countries are more pronounced for point-and-shoot than for digital single-lens reflex users. Fourth, there was again a distinct difference between arbitrary friends and high cliqueness friends in terms of prediction quality for conversion.

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  1. Camera Brand Congruence and Camera Model Propagation in the Flickr Social Graph

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      cover image ACM Transactions on the Web
      ACM Transactions on the Web  Volume 5, Issue 4
      October 2011
      154 pages
      ISSN:1559-1131
      EISSN:1559-114X
      DOI:10.1145/2019643
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 01 October 2011
      Accepted: 01 February 2011
      Revised: 01 December 2010
      Received: 01 February 2010
      Published in TWEB Volume 5, Issue 4

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      Author Tags

      1. Brand congruence
      2. brand loyalty
      3. flickr
      4. social networks

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      • (2025)Assessing recreationists’ preferences of the landscape and species using crowdsourced images and machine learningLandscape and Urban Planning10.1016/j.landurbplan.2025.105315257(105315)Online publication date: May-2025
      • (2023)Social media and deep learning reveal specific cultural preferences for biodiversityPeople and Nature10.1002/pan3.104665:3(981-998)Online publication date: 20-Mar-2023
      • (2014)Triangulating Social Multimedia Content for Event Localization using Flickr and TwitterTransactions in GIS10.1111/tgis.1212219:5(694-715)Online publication date: 16-Nov-2014

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