Abstract
Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic’s importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends.
Highlights
The majority of people, no matter what their role or position is, exchange a lot of information via peer to peer communication based on messaging services such as Email, Twitter, Facebook, Google+, etc
As an example of possible applications, our analysis has focused on the emerging Hadoop market, starting with an inspection of Google Trends data and single page click-count data from Wikipedia in Fig 3c and 3d
What are the benefits of using the time resolved relevance index TRRI? First, as presented above, in Google search data there exists a hidden bias as it is unknown how other keywords with a strong relation influence trends
Summary
The majority of people, no matter what their role or position is, exchange a lot of information via peer to peer communication based on messaging services such as Email, Twitter, Facebook, Google+, etc. Since the numbers of hypertext pages and hyperlinks in the WWW have been continuously growing for more than 20 years, the problem of finding relevant content has become increasingly important. This led, for example, to the growth of Google Inc. with its mission statement ‘to organize the world’s information and make it universally accessible and useful’. The appearance of Social Media Applications (SMA), such as Facebook, LinkedIn, and Twitter, with friendship and follower relations between individual users has led to the creation and simultaneous evolution of novel user community networks (social networks) together with content networks. Additional problems arise if the time evolution of content relevance shall be traced and if local and global relevance of content shall be distinguished
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