| Title |
Data Mining and Privacy of Social Network Sites’ Users: Implications of the Data Mining Problem
|
|---|---|
| Published in |
Science and Engineering Ethics, June 2014
|
| DOI | 10.1007/s11948-014-9564-6 |
| Pubmed ID | |
| Authors |
Yeslam Al-Saggaf, Md Zahidul Islam |
| Abstract |
This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 119 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 31 | 26% |
| Student > Bachelor | 24 | 20% |
| Student > Ph. D. Student | 8 | 7% |
| Student > Doctoral Student | 7 | 6% |
| Researcher | 6 | 5% |
| Other | 15 | 13% |
| Unknown | 28 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 35 | 29% |
| Business, Management and Accounting | 17 | 14% |
| Social Sciences | 10 | 8% |
| Psychology | 4 | 3% |
| Engineering | 4 | 3% |
| Other | 19 | 16% |
| Unknown | 30 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 17 June 2014.
All research outputs
#19,440,618
of 23,911,072 outputs
Outputs from Science and Engineering Ethics
#835
of 947 outputs
Outputs of similar age
#168,538
of 232,194 outputs
Outputs of similar age from Science and Engineering Ethics
#15
of 15 outputs
Altmetric has tracked 23,911,072 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 947 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.3. This one is in the 3rd percentile – i.e., 3% of its peers scored the same or lower than it.
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 232,194 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.