| Title |
Web accessibility support for visually impaired users using link content analysis
|
|---|---|
| Published in |
SpringerPlus, March 2013
|
| DOI | 10.1186/2193-1801-2-116 |
| Pubmed ID | |
| Authors |
Hajime Iwata, Naofumi Kobayashi, Kenji Tachibana, Junko Shirogane, Yoshiaki Fukazawa |
| Abstract |
Web pages are used for a variety of purposes. End users must understand dynamically changing content and sequentially follow page links to find desired material, requiring significant time and effort. However, for visually impaired users using screen readers, it can be difficult to find links to web pages when link text and alternative text descriptions are inappropriate. Our method supports the discovery of content by analyzing 8 categories of link types, and allows visually impaired users to be aware of the content represented by links in advance. This facilitates end users access to necessary information on web pages. Our method of classifying web page links is therefore effective as a means of evaluating accessibility. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 50% |
| Australia | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 4% |
| Germany | 1 | 4% |
| Unknown | 21 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 22% |
| Researcher | 4 | 17% |
| Lecturer > Senior Lecturer | 2 | 9% |
| Student > Bachelor | 2 | 9% |
| Student > Postgraduate | 2 | 9% |
| Other | 6 | 26% |
| Unknown | 2 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 52% |
| Business, Management and Accounting | 2 | 9% |
| Engineering | 2 | 9% |
| Social Sciences | 2 | 9% |
| Nursing and Health Professions | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 1 | 4% |
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 29 March 2013.
All research outputs
#13,148,117
of 22,701,287 outputs
Outputs from SpringerPlus
#653
of 1,852 outputs
Outputs of similar age
#113,598
of 215,834 outputs
Outputs of similar age from SpringerPlus
#34
of 135 outputs
Altmetric has tracked 22,701,287 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 1,852 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 63% of its peers.
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 215,834 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.