Influentials, Networks, and Public Opinion Formation

Citation

Watts, Duncan J., and Peter Sheridan Dodds. "Influentials, networks, and public opinion formation." Journal of consumer research 34.4 (2007): 441-458.

Online version

[1]

Problem

The problem is to study the process of public opinion formation. Specifically it casts a challenge to the classical "two-step" flow model which claims the minority of influentials (a.k.a. opinion leaders) play crucial role in diffusing information between the media and the majority society and the author argues that the responsibility of the influentials of the two-step flow is considerably overestimate and in fact the influentials are only modestly more important than average individuals.

Idea

The basic idea is to qualitatively study the problem on their proposed models to validate (or invalidate) the claims in two-step flow model.

Method

The proposed model assumes that each individual makes a binary decision e.g. agree or disagree whose decision would be affected by the decision of the others (called positive externalities). As a result, the information is diffused on a so-called influence network where each person is a node and the directed edge between node a and b indicates a's decision influence the decision of b. The model is different from the two-step model in two aspects: the opinion of the majorities could in turn affect the opinion of the influential and it takes more steps to propagate through the proposed model than through the two-step model which in fact only requires two steps of propagation.

Fig.2 Tracking 50 largest threads

Important Assumptions

The author made some important assumption in their basic model

1. The edges in the influence graph are randomly selected.
2. Given a node $i\,$, the number of its neighbors $n_{i}\,$ is drawn from an Poisson distribution.
3. The derision of an individual is determined by a piece-wise threshold function.

Basic Model

The basic model is established on the above three assumptions.

Data set

No real-world dataset is presented in the paper and all datasets used in the study are synthetic.

Result

From the above figure we can not only obtain a clue about the news cycle but also get an idea about the popular news in each period. In addition, the authors also conclude their findings by global analysis and local analysis.

Notes

[2] Support website

[3] J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, M. Hurst. Cascading behavior in large blog graphs.SDM’07.

[4] X. Wang and A. McCallum. Topics over time: a non-markov continuous-time model of topical trends.Proc. KDD, 2006.

[5] X. Wang, C. Zhai, X. Hu, R. Sproat. Mining correlated bursty topic patterns from coordinated text streams.KDD, 2007.