Web Query Analysis: Aligning Queries to Periodic Events

1
Web Query Analysis Aligning Queries to Periodic
Events Student Trinh Hoang Minh
Supervisor Dr. Min-Yen Kan
Project IDH079370
Overview
Data Collection

• We collected 29 popular queries which represent
  annual events and 39 related queries to those
  events.
• Using the popular trend search from Google
  Trends, we obtain the query volume histogram
  images. Numerical data are then extracted from
  downloaded images using a graph digitizer.

• We describe a method to determine the temporal
  correlation between web queries. In particular
• Study how to identify periodic query-as-events
• Correlate other non-periodic queries to these
  events.
• Develop a prototype to analyze such temporal
  correlation between queries and assess its
  performance, resulting in over 90 accuracy.

Evaluation

• Two classifiers has been trained, a periodic
  classifier and a correlation classifier.
• Periodic Classifier Evaluation
• Obtain judgments from 7 staff members for all
  68 queries, resulting in Fleiss Kappa score of K
  0.802.
• High true positive value of 93.1, with K
  0.794 when compared with the human judges.
• Correlation Classifier Evaluation
• divide the queries into periodic and
  non-periodic categories based on previous
  classification.
• apply the second correlation classifier on
  pairs of queries, one drawn from each class.
• Each pair was manually classified as to whether
  the two queries were thought to be correlated.
• High true positive rate of 93.3, with Fleiss
  Kappa score 0.70 when compared with human
  judges.

Task 1. Periodic Classification

• A recurring event has regular, repeated peaks in
  its histogram, corresponding to the events
  actual date.
• We train a supervised Bayesian Network
  Classifier, using two main features
• Autocorrelation Function(ACF) with a lag value
  k set equivalent to one year.
• Correlation Coefficient Value (CCV) of
  pair-wise yearly histograms (2005, 2006, 2007).
  To reduce noise and variability, Dynamic time
  warping (DTW) was applied to find the best match
  among yearly histograms.

Task 2. Temporal Correlation Classification

• To identify whether other (non-periodic) queries
  are correlated to these periodic
  events-as-queries, we again used supervised
  classification, using four main of features.
• Overall Correlation calculate the full period
  query histogram correlation coefficient to find
  out the temporal correlation coefficient.
•  
• Most Recent Year Correlation The correlation
  coefficient for the last 12 months (i.e. 2007) is
  calculated and treated as a separate feature.
• Conjunctive Data Two features measure the
  strength of the conjoined queries
• the number of the web search results found a
  search engine (Googles Search API, in our case).
  
• the number of times the two queries appear
  together in the top ten titles.

Conclusion

• Key period Correlation A key period is
  defined as a period with high search volume,
  relative to other periods.
• We then apply correlation coefficient equation
  during these key periods only.

• Contribution
• Periodic query classification
• Temporal correlation for web queries Correlate
  queries to periodic events with reasonable
  accuracy, using only relative volume histograms
  and search results.
• Facilitate proactive query suggestion or
  re-ranking of search results, which we are
  planning to explore as applications.
• Future work
• extend our work by integrating more data on
  query trends from news and blog trends.
• extend our work to use partial correlation to
  correct for overall query volume growth.