Like I said previously, Wikipedia provides a mine of data worth digging into when researching a topic. It is user-generated, well-moderated and heavily interlinked – all three factors contribute to the fact that the site can be quite useful for keyword and niche research.
This time I am looking into Wikipedia visualization tools that can help map the topic and also provide you with ideas where to look further (related and neighboring terms):
Wikipedia-roll visualizes Wikipedia articles by distinct concepts (sections) and items. For example, for “search engine optimization” we get:
- Key elements;
- Histories;
- Webmasters with search engines;
- As a marketing strategy;
- White hat versus black hat;
- International markets;
- See also;
- Legal precedents;
- References.
VisWiki connects Wikipedia articles via interactive maps. You can thus easily see and click through related topics and terms:
For “search engine optimization” we see the following map (click on “Related” for more results):
EyePlorer (previously reviewed by me on SEJ) visualizes knowledge graphs (k-graphs) derived from Wikipedia content that can be interactively explored. The knowledge graph consists of eyespots representing concepts that are connected to your topic. For better orientation those eyespots are clustered.
You can research the topic by removing nay cluster. You can also explore the connections between eyespots by clicking on any of them:
WikiRank is a newly launched tool with a yet different concept from the above ones (but I couldn’t help mentioning it here): it visualizes the page / term popularity on Wikipedia.
Apart from trending how popular the topic has been for the past month, the tool also allows to compare many topic popularity.
Note though that the information provided is “based on logs from Wikipedia’s HTTP Squid proxy servers. That means every single page load is recorded, whether initiated by a human with a browser or a Web spider crawling through”. [Thanks to Search Rank for the warning] So the actual (absolute) impressions numbers are not so important as the trend itself (relative increases or decreases for any particular term).
