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Making social networks more human: A topological approach
Abstract
A key problem in social network analysis is to identify nonhuman interactions. State-of-the-art bot-detection systems like Botometer train machine-learning models on user-specific data. Unfortunately, these methods do not work on data sets in which only topological information is available. In this paper, we propose a new, purely topological approach. Our method removes edges that connect nodes exhibiting strong evidence of non-human activity from publicly available electronic-social-network datasets, including, for example, those in the Stanford Network Analysis Project repository (SNAP). Our methodology is inspired by classic work in evolutionary psychology by Dunbar that posits upper bounds on the total strength of the set of social connections in which a single human can be engaged. We model edge strength with Easley and Kleinberg's topological estimate; label nodes as “violators” if the sum of these edge strengths exceeds a Dunbar-inspired bound; and then remove the violator-to-violator edges. We run our algorithm on multiple social networks and show that our Dunbar-inspired bound appears to hold for social networks, but not for nonsocial networks. Our cleaning process classifies 0.04% of the nodes of the Twitter-2010 followers graph as violators, and we find that more than 80% of these violator nodes have Botometer scores of 0.5more »
- Authors:
-
[1];
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Univ. of New Mexico, Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1559509
- Report Number(s):
- SAND2019-9300J
Journal ID: ISSN 1932-1864; 678306
- Grant/Contract Number:
- AC04-94AL85000
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Statistical Analysis and Data Mining
- Additional Journal Information:
- Journal Name: Statistical Analysis and Data Mining; Journal ID: ISSN 1932-1864
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Berry, Jonathan W., Phillips, Cynthia A., and Saia, Jared. Making social networks more human: A topological approach. United States: N. p., 2019.
Web. doi:10.1002/sam.11420.
Berry, Jonathan W., Phillips, Cynthia A., & Saia, Jared. Making social networks more human: A topological approach. United States. doi:10.1002/sam.11420.
Berry, Jonathan W., Phillips, Cynthia A., and Saia, Jared. Wed .
"Making social networks more human: A topological approach". United States. doi:10.1002/sam.11420.
@article{osti_1559509,
title = {Making social networks more human: A topological approach},
author = {Berry, Jonathan W. and Phillips, Cynthia A. and Saia, Jared},
abstractNote = {A key problem in social network analysis is to identify nonhuman interactions. State-of-the-art bot-detection systems like Botometer train machine-learning models on user-specific data. Unfortunately, these methods do not work on data sets in which only topological information is available. In this paper, we propose a new, purely topological approach. Our method removes edges that connect nodes exhibiting strong evidence of non-human activity from publicly available electronic-social-network datasets, including, for example, those in the Stanford Network Analysis Project repository (SNAP). Our methodology is inspired by classic work in evolutionary psychology by Dunbar that posits upper bounds on the total strength of the set of social connections in which a single human can be engaged. We model edge strength with Easley and Kleinberg's topological estimate; label nodes as “violators” if the sum of these edge strengths exceeds a Dunbar-inspired bound; and then remove the violator-to-violator edges. We run our algorithm on multiple social networks and show that our Dunbar-inspired bound appears to hold for social networks, but not for nonsocial networks. Our cleaning process classifies 0.04% of the nodes of the Twitter-2010 followers graph as violators, and we find that more than 80% of these violator nodes have Botometer scores of 0.5 or greater. Furthermore, after we remove the roughly 15 million violator-violator edges from the 1.2-billion-edge Twitter-2010 follower graph, 34% of the violator nodes experience a factor-of-two decrease in PageRank. PageRank is a key component of many graph algorithms such as node/edge ranking and graph sparsification. Thus, this artificial inflation would bias algorithmic output, and result in some incorrect decisions based on this output.},
doi = {10.1002/sam.11420},
journal = {Statistical Analysis and Data Mining},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {7}
}
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DOI: 10.1002/sam.11420
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