Studying the viral growth of a connective action network using information event signatures
Bibliographic Data
| ID | 15116156 |
|---|---|
| Authors | Jeff Hemsley (0000-0001-9276-6983, Syracuse University, corresponding author) |
| Year | 2016 |
| Publication date | 2016-07-16 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | First Monday (JOURNAL) |
| Journal identifiers | ISSN: 1396-0458 • E-ISSN: 1396-0466 |
| Publisher | University of Illinois Libraries (PUBLISHER) |
| DOI | 10.5210/fm.v21i8.6650 |
| OpenAlex | W2497249804 |
| Language | EN |
| Citations received | 7 |
The Arab spring and Occupy Wall Street movements demonstrated that networks of individuals who share interests or grievances could quickly form on social media. There is a reciprocal relationship between the growth of these networks and the information that flows through them. This study examines this relationship by using viral information event signatures, which show the changing rate of sharing of a specific message over a period of time. The Occupy movement and the digital interactions of its participants provides a context and rich corpus of data from which to study the relationship between the signatures of information flows and the growth the Occupy network. Using exploratory data analysis and multivariate regression to analyze Occupy related tweets drawn from a corpus of over 64 million tweets, this study first provides a parameterized signature model and then uses regression to show that a relationship exists between the shape of the signature and the rate at which key actors gain followers. This work also finds a quadratic decline, over the life cycle of the movement, in the rate at which the actors gain followers. The contributions of this work include the parameterized signature model, a demonstration of its usefulness, and a new perspective on the growth of the Occupy movement
Collective action · Econometrics · Geography · Machine learning · Multivariate statistics · Political science · Reciprocal · Social media · Social network analysis · World Wide Web · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence
| Unique citing works | 7 |
|---|---|
| Citations per year | 0,88 |
| Citation span | 2018 - 2024 (7) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 6 |