| Title |
Detection of strong attractors in social media networks
|
|---|---|
| Published in |
Computational Social Networks, December 2016
|
| DOI | 10.1186/s40649-016-0036-9 |
| Pubmed ID | |
| Authors |
Ziyaad Qasem, Marc Jansen, Tobias Hecking, H. Ulrich Hoppe |
| Abstract |
Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing. The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time. We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs. Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 33% |
| Germany | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Postgraduate | 4 | 19% |
| Student > Ph. D. Student | 4 | 19% |
| Lecturer | 2 | 10% |
| Student > Master | 2 | 10% |
| Student > Doctoral Student | 1 | 5% |
| Other | 4 | 19% |
| Unknown | 4 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 33% |
| Business, Management and Accounting | 2 | 10% |
| Mathematics | 1 | 5% |
| Environmental Science | 1 | 5% |
| Arts and Humanities | 1 | 5% |
| Other | 4 | 19% |
| Unknown | 5 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 January 2018.
All research outputs
#13,258,789
of 22,908,162 outputs
Outputs from Computational Social Networks
#16
of 40 outputs
Outputs of similar age
#205,204
of 419,655 outputs
Outputs of similar age from Computational Social Networks
#2
of 3 outputs
Altmetric has tracked 22,908,162 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 40 research outputs from this source. They receive a mean Attention Score of 3.9. This one scored the same or higher as 24 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 419,655 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.