Why we search: visualizing and predicting user behavior

By Eytan Adar, Daniel S. Weld, Brian N. Bershad, S. Gribble. 2007

129 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jure Leskovec
2. Tianhao Wang
3. Jaewon Yang
4. Meredith Ringel Morris
5. Somesh Jha
6. Ninghui Li
7. Jaime Teevan
8. Ciro Cattuto

In Computer Vision and Pattern Recognition

1. Jure Leskovec
2. Jaewon Yang
3. Fabrizio Silvestri
4. Jaime Teevan
5. Meredith Ringel Morris
6. Somesh Jha
7. Efthimis N. Efthimiadis
8. Daniel J. Liebling

In Statistical and Nonlinear Physics

1. Jure Leskovec
2. Jaewon Yang
3. Meredith Ringel Morris
4. Jaime Teevan
5. Bruno Gonçalves
6. Ciro Cattuto
7. Fabrizio Silvestri
8. Daniel J. Liebling

Citations in the corpus

• Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls (2014, background): “As things stand, these exist (Adar et al., 2007 or Kairam, 2013) but are rare.”
• Conversational tagging in twitter (2010, background): “[1] studied the general trends for queries in several datasets of queries, blog posts, and news articles.”
• Patterns of temporal variation in online media (2011, background): “that find temporal correlation between social media [2] or queries whose temporal variations are similar each other [8].”
• Towards Supporting Search over Trending Events with Social Media (2013, methodology): “Method We find the maximum cross-correlation between the two time-series for an event using a method similar to that developed by Adar et al. (2007). For all values of h in a given range, we shift one time-series by h hours and calculate the correlation; the value of h maximizing the correlation rep”
• Dynamical classes of collective attention in twitter (2011, background): “In particular, specific features of the popular item under consideration can now be related to its activity profile by means of semantic analysis and natural language processing of the messages exchanged by the users [3, 15, 16].”
• Locally Differentially Private Frequent Itemset Mining (2018, background): “, mining association rules [4], predicting user behavior [3], and finding correlations [9].”
• Mining Query Logs: Turning Search Usage Data into Knowledge (2010, background): “[2] use time series to predict (i) why users issue queries and (ii) how users react and cause news spreading.”