The DOI matches its Crossref record; 59 of the 63 references it cites were found in a registry or an index.
How Visibility and Divided Attention Constrain Social Contagion
Open accessAbstract
How far and how fast does information spread in social media? Researchers have recently examined a number of factors that affect information diffusion in online social networks, including: the novelty of information, users' activity levels, who they pay attention to, and how they respond to friends' recommendations. Using URLs as markers of information, we carry out a detailed study of retweeting, the primary mechanism by which information spreads on the Twitter follower graph. Our empirical study examines how users respond to an incoming stimulus, i.e., a tweet (message) from a friend, and reveals that dynamically decaying visibility, which is the increasing cognitive effort required for discovering and acting upon a tweet, combined with limited attention play dominant roles in retweeting behavior. Specifically, we observe that users retweet information when it is most visible, such as when it near the top of their Twitter feed. Moreover, our measurements quantify how a user's limited attention is divided among incoming tweets, providing novel evidence that highly connected individuals are less likely to propagate an arbitrary tweet. Our study indicates that the finite ability to process incoming information constrains social contagion, and we conclude that rapid decay of visibility is the primary barrier to information propagation online.
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“Given the dynamic nature of social media, its large size, and individuals' finite ability to process incoming information (Oken Hodas and Lerman 2012), we should expect Twitter users to maximize the value of their online experience by choosing to follow political actors who can provide information that can be of higher value to them.”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Oken Hodas and Lerman 2012
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Cites
Stochastic Processes in Physics and Chemistry
(2007) · Elsevier eBooks · doi:10.1016/b978-0-444-52965-7.x5000-4 · Cited by 9,096
Egeth, H. E., & Kahneman, D. J. (1975) · The American Journal of Psychology · doi:10.2307/1421603 · Cited by 6,976
Newman (2005) · doi:10.1080/00107510500052444 · Cited by 5,749
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
The spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692
Self-control as a limited resource: Regulatory depletion patterns.
Muraven, M., Tice, D. M., & Baumeister, R. F. (1998) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.74.3.774 · Cited by 1,724
Everyone's an influencer: quantifying influence on twitter
Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935845 · Cited by 1,689
Pashler, H. (1997) · doi:10.7551/mitpress/5677.001.0001 · Cited by 1,607
Modeling the role of salience in the allocation of overt visual attention
Parkhurst, D. J., Law, K., & Niebur, E. (2002) · Vision Research · doi:10.1016/s0042-6989(01)00250-4 · Cited by 1,435
Decision making and the avoidance of cognitive demand
Kool, W., McGuire, J. T., Rosen, Z. B., & Botvinick, M. M. (2010) · Journal of Experimental Psychology: General · doi:10.1037/a0020198 · Cited by 1,141
Flagged first, then by citations
4 of the 63 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- newman, 2005, Power laws Pareto Distributions and Zipf's law, 46, 323
- goncalves, 2011, Validation of Dunbar's Number in Twitter Conversations
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Kang, J.-H., & Lerman, K. (2013) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2481492.2481498 · Cited by 4
Self-citationInteractional and Informational Attention on Twitter
Baltzer, A., Karsai, M., & Roth, C. (2019) · Information · doi:10.3390/info10080250 · Cited by 4
Observation of Matthew Effects in Sina Weibo microblogger
Yang, M., Zhou, Y., Zhou, Q., Chen, K., He, J., & Yang, X. (2013) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2013.6691796 · Cited by 4
Competing Opinions and Stubbornness: Connecting Models to DataPreprint
Burghardt, K., Rand, W. M., & Girvan, M. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2536235 · Cited by 4
Evans, J., Silk, M. J., Boogert, N. J., & Hodgson, D. J. (2020) · doi:10.32942/osf.io/zvkgu · Cited by 4
Improving Social Bot Detection Through Aid and Training
Kenny, R., Fischhoff, B., Davis, A. L., & Canfield, C. (2023) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208231210145 · Cited by 4
Inferring Predictive Links in Social Media Using Content Transfer
Steeg, G. V., & Galstyan, A. (2012) · Cited by 3
An Agent-Based Model of Posting Behavior During Times of Societal Unrest
Bathina, K. C., Jammalamadaka, A., Xu, J., & Lu, T.-C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-60240-0_6 · Cited by 3
Tie-decay temporal networks in continuous time and eigenvector-based centralitiesPreprint
Ahmad, W., Porter, M. A., & Beguerisse-Díaz, M. (2018) · arXiv (Cornell University) · Cited by 3
Consequential Ranking Algorithms and Long-term WelfarePreprint
Tabibian, B., Gómez, V., De, A., Schölkopf, B., & Gomez-Rodriguez, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.05305 · Cited by 3
Bagrow, J. P., Danforth, C. M., & Mitchell, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06361 · Cited by 3
Amin, Z., Ali, N. M., Zinaida, R. S., & Helmi, S. (2024) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v10i1.4616 · Cited by 2
Reconstructing Online Behaviors by Effort Minimization
Rad, A. A., & Rahmandad, H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37210-0_9 · Cited by 2
The Impacts of Network Structure on User Activity Level in Online Social Networks
Long, Y., Li, V. O. K., Niu, G., & Lu, Z. (2015) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2015.201 · Cited by 2
Maintaining intellectual diversity in data science
Mann, R. P., & Woolley-Meza, O. (2017) · Data Science · doi:10.3233/ds-170003 · Cited by 2
The behavior of epidemics under bounded susceptibility
Krishnasamy, S., Banerjee, S., & Shakkottai, S. (2014) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/2637364.2591977 · Cited by 2
Tang, J., Zhao, F., Zhang, R., Chai, B., & Di, S. (2021) · International Journal of Modern Physics C · doi:10.1142/s0129183121500790 · Cited by 2
Information-Theoretic Measures of Influence Based on Content DynamicsPreprint
Steeg, G. V., & Galstyan, A. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1208.4475 · Cited by 2
Stephen, A. T., Zubcsek, P. P., & Goldenberg, J. (2016) · Cited by 1
On the Users’ Efficiency in the Twitter Information Network
Babaei, M., Grabowicz, P. A., Valera, I., & Gomez-Rodriguez, M. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14657 · Cited by 1
Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks
Baral, N., Gunaratne, C., Jayalath, C., Rand, W. M., Senevirathna, C., & Garibay, I. I. (2021) · Springer proceedings in complexity · doi:10.1007/978-3-030-77517-9_16 · Cited by 1
Does geographical location have an impact on data samples extracted from Twitter?
Ivanova, R. V., Sobernig, S., & Strembeck, M. (2022) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams58071.2022.10062544 · Cited by 1
Fukushima, M. (2026) · arXiv · doi:10.48550/arxiv.2609.19183
“In online discussions the same limit appears as divided attention capping how many contributions a participant actually processes (Hodas and Lerman, 2012).”
Roy, S. D., & Zeng, W. (2014) · Springer eBooks · doi:10.1007/978-3-319-09117-4_7
Comparison on Attention Automaton Sensing Collective user Interests in Social Network Communities
Poornima, S., & Rangaraj, R. (2015) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21601180
On cross-domain social semantic learning
Roy, S. D. (2013) · doi:10.32469/10355/42943
Collective Attention towards Scientists and Research Topics
Wagner, C., Zagovora, O., Sennikova, T., & Karimi, F. (2018) · ACM Web Science Conference (WebSci) · doi:10.1145/3201064.3201097
Predicting and Explaining Behavioral Data with Structured Feature Space DecompositionPreprint
Fennell, P. G., Zuo, Z., & Lerman, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.09841
Self-citationZhu, L. (2020) · Otago University Research Archive (University of Otago)
Attention and misinformation sharing on social mediaPreprint
Amin, Z., Ali, N. M., & Smeaton, A. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12593
Do personality traits drive online commitment to vote in social networks
Wood, M. A. (2019) · Minerva Access (University of Melbourne)
Identifying Shifts in Collective Attention to Topics on Social Media
He, Y., Rao, A., Burghardt, K., & Lerman, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80387-2_22
Self-citationYang, Y., Ta, N., Li, K.-Y., Jiao, F., Hu, B.-J., & Li, Z. (2020) · Journal of Medical Internet Research · doi:10.2196/25702
Developing natural language processing instruments to study sociotechnical systemsPreprint
Lab, S. (2021) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/zrbd4
Birth of a scapegoat: An actor‐affect‐affordance model of symbolic attribution in the digital age
Wippell, J. G. R. (2026) · Political Psychology · doi:10.1111/pops.70163
Scheduling Broadcasts in a Network of TimelinesPreprint
Manzoor, E., Kwak, H., & Kalnis, P. (2015) · arXiv (Cornell University) · doi:10.25781/kaust-sfazp
Metadata
- Authors
- Hodas, N. O., & Lerman, K.
- Venue
- 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing
- Publisher
- IEEE
- Published
- 2012-09
- Pages
- 249-257
- OpenAlex
- W1965563190
- Cited by
- 136
- References
- 63
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 5 · 2022: 2 · 2021: 13 · 2020: 14 · 2019: 9 · 2018: 13 · 2017: 12 · 2016: 14 · 2015: 17 · 2014: 16 · 2013: 15 · 2012: 2
- FWCI
- 13.80
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Social Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Keywords
- information dissemination, social contagion, retweets, Twitter, divided attention, cognitive effort, social networking sites, user attention
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