Characterising and evaluating dynamic online communities from live microblogging user interactions
Bibliographic Data
| ID | 4683917 |
|---|---|
| Authors | Hugo Hromic (0000-0001-6535-246X, Ollscoil na Gaillimhe – University of Galway, corresponding author), Conor Hayes (0000-0002-9152-4138, Ollscoil na Gaillimhe – University of Galway) |
| Year | 2019 |
| Volume | 9 |
| Issue | 1 |
| Publication date | 2019-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-019-0576-8 |
| OpenAlex | W2953409433 |
| Language | EN |
| Citations received | 4 |
| References cited | 41 |
Microblogging social media focuses on fast open real-time communication using short messages between users and their followers. These platforms generate large amounts of content, and community finding techniques are a suitable alternative for organising it. However, there is no clear agreement in the literature for a definition of user community for the microblogging use case, leading to unreliable ground-truth data and evaluation. In this work, we differentiate between functional and structural definitions of communities for microblogging. A functional community groups its users by a common independent social function, e.g. fans of the same football team, while in a structural community the members exclusively depend on their connectivity in a network, e.g. modularity. We build and characterise eight types of functional communities to be used as user-labelled ground-truth and five types of user interactions networks from Twitter. We then evaluate—in static and dynamic scenarios—thirteen popular structural community definitions using five different Twitter datasets, exploring their goodness and robustness for detecting the functional ground-truth under different perturbation strategies. Our results show that definitions based on internal connectivity, e.g. Triangle Participation Ratio, Fraction Over Median Degree or Conductance work best for the Twitter use case and are very robust. On the other hand, other scores such as Modularity are limited and do not perform well due to the sparsity and noise of microblogging. Furthermore, using user activity as basis to separate communities into active hotspots further improves the performance of community detection in microblogging
Community structure · Data mining · Data science · Ground truth · Information retrieval · Machine learning · Microblogging · Online community · Social media · World Wide Web · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Peer-to-Peer Network Technologies
Maps of random walks on complex networks reveal community structure
Earthquake shakes Twitter users
Quantifying social group evolution
Resolution limit in community detection
Normalized cuts and image segmentation
Uncovering the overlapping community structure of complex networks in nature and society
Community detection in graphs
Social media? Get serious! Understanding the functional building blocks of social media
Why social networks are different from other types of networks
Why we twitter
Defining and identifying communities in networks
Collective dynamics of ‘small-world’ networks
Modularity and community structure in networks
Birds of a Feather
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2020 - 2023 (4) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |