About me
Courses & Divulgation
aaa         a
Differences between web sessions according to the origin of their visits

Jose Luis Ortega

R&D Analysis, Vice-presidency for Science and Technology, CSIC, Serrano, 113, 28006, Madrid, Spain,

Isidro Aguillo

Cybermetrics Lab, CCHS-CSIC, Albasanz, 26-28, 28037, Madrid, Spain

Cite as: Ortega, J. L., Aguillo, I. F. (2010), Differences between web sessions according to the origin of their visits. Journal of Informetrics, 4(3): 331-337


     The aim of this paper is to characterize the distribution of number of hits and spent time by web session. It also expects to find if there are significant differences between the length and the duration of a session with regard to the point of access -search engine, link or root. Web usage mining was used to analyse 17,174 web sessions that were identified from the webometrics.info web site. Results show that both distribution of length and duration follow an exponential decay. Significant differences between the different origins of the visits were also found, being the search engines’ users those who spent most time and did more clicks in their sessions. We conclude that a good SEO policy would be justified, because search engines are the principal intermediaries to this web site.


     One of the most important fields of the Informetrics, and one of the less studied, is the analysis of the information usage of web sites. Through the analysis of web log data we can analyse the behaviour of a web surfer in a web site, extracting navigational patterns about their favourites pages, describing the used paths in order to access to relevant information and checking the reliability of our web design and architecture. Although business and commercial web sites have paid soon attention to the gathering and processing of information about the behaviour of their customers (Gomory et al., 1999), as an extension of the data mining techniques applied in their client databases, this academic field has attracted little interest mainly due to the difficulty of obtaining this type of data and comparing similar patterns of different web log sources.
     Even then, web log analyses have been carried out in order to improve the web design (Spiliopoulou, 2000), as a way to evaluate the quality of library catalogues (Peters, 1993; Kurth, 1993) or to understand information flows (Thelwall, 2001). The first studies were focused in the functionality of the search engines, describing query patterns in Altavista (Silverstein et al., 1998; 1999; Anick, 2003), Excite and Alltheweb (Jansen and Spink, 2006) and Yahoo! (Teevan et al., 2006). However, several studies were focused more in the definition of web sessions than in the number of hits. Data mining was used to the identification of web sessions (Cooley et al., 1997; 1999), to estimate their duration and their length in clicks (Pitkow, 1997; He et al., 2002) and to classify content according to the pages requested by their visitors (Wang and Zaļane, 2002). Several techniques have been used to improve and help the session extraction and processing such as the statistical language model (Huang et al., 2004), fuzzy logic (Nasraoui et al., 1999) and Markov models (Deshpande and Karypis, 2004). Other papers have addressed the visualization of web sessions as a way to uncover navigational patterns (Hochheiser and Shneiderman, 1999; Lam et al., 2007).
     Few papers have characterized the distribution of sessions according to the number of hits or the time spent in the web site. Markov and Larose (2007) compared the web logs of the Central Connecticut University State and the Environmental Protection Agency (EPA) web sites, finding an exponential decay in the distributions of session’s duration and length. Other works also found skewed distributions in the length and duration of the web search sessions (Cooley, 1999; Jansen et al., 2005). Nevertheless, there are no works that have shown how the point of access (entry page) or referrer page affects to the further navigation in the website.


     The aim of this paper is to solve the following questions:

  • Is it possible to know how many sessions have a length of certain clicks?
  • Is it possible to characterize and estimate the number of sessions that last certain time?
  • Is it the length of a session different according to the point of access of each user?
  • Is it the time that a user spent in a website different according to the point of access used?


     Data processing
     The Web ranking of World universities (
webometrics.info) is a website that ranks 6,000 universities according to two main criteria: size (number of pages and rich files) and visibility (number of incoming links). It is the most complete and updated ranking of universities web domains. This website is very popular with more than 3 million visitors per year and a Page Rank of 8. We think that the high visibility of this site provides a good sample to study the access pattern of a website.
     Web log transactions from 2006 July were selected as a sample to carry out our session analysis. The web log file was cleaned according to several criteria, removing the following accesses:

  • To graphic files (gif, jpg and png)
  • To style sheets (css)
  • Which do not request a petition (get)
  • From the own website editor IPs (161.111.200.*)
  • Made by crawlers or bots (Googlebot, Msnbot, Slurp, Gigabot, etc.)
     After this process, 526,004 unique accesses were identified. To rebuild the performed sessions, we used the web usage mining technique (Cooley et al., 1997, 1999). This technique is developed in three stages:
  • User identification: Although webometrics.info does not allow to identify users through cookies or registration, we identified each user through a unique IP.
  • Session identification: We established a time limit of 30 min. (Catledge & Pitkow, 1995). Although a session may be more than 30 min., this standard measure allows us to separate sessions with the same IP.
  • Session rebuilding: Some accesses are not registered in the log file because there are cache and proxies copies in order to do not saturate the web server. These accesses have been rebuilt through the web site architecture.

     After apply web usage mining techniques, we have identified 14,174 sessions

IP Date Time Access Referrer
202.174.136.* 26/06/2006 9:33:49 /top3000.asp.htm http://www.google.com.hk/search?q=webometrics...
202.174.136.* 26/06/2006 9:50:46 /top3000.asp-offset=50.htm http://www.webometrics.info/top3000.asp.htm
202.174.136.* 26/06/2006 9:53:54 /university_by_country_select.asp.htm http://www.webometrics.info/top3000.asp-offset=50.htm
202.174.136.* 26/06/2006 9:55:16 /methodology.html http://www.webometrics.info/top3000.asp-offset=50.htm
Table 1. An example of web session with length 4 and from a search engine

     We defined three main ways to access to a website: 1) through a search engine request: a query launched to a search engine allows to retrieve a link to the website demanded; 2) through a web link: surfing the web we may access to a website through a link from other website; 3) through the website root: typing in our web browser the URL of the website demanded. So, we classified the sessions in accesses through a search engine, through a web link and typing the URL (root). The origin of each session was detected through the referrer field in the log file. So, we observed the referrer of the access that originated the session. For example, this session (Table 1) was created from a search engine query, and then it was classified in the search engine category.
    We also classified each session according to its length. We define the length as the number of access carried out by the same IP in an interval less than 30 min. This timeout has been defined as a standard measure in several studies (Mahoui and Cunningham, 2000; Mat-Hassan and Levene, 2005; Jansen et al. 2007) from the empirical observations of Catledge and Pitkow (1995). This convention is used because it is impossible to observe when a user leaves a web page form the web log data. This lack of data affects to the exact definition of the session length because we can not know how many time one user spends in the last page (Huntington, Nicholas & Jamali, 2008). Due to this, the last view was not computed. We understand that this technical limitation is the same for the three types of access, so the differences in the session length will be the same in root, link and search engine access. The above session (Table 1) was defined as 4 length session, because the fifth access was made after than 30 min. and it includes four accesses in less than 30 min. between them. As we said before, this is the limit to separate different sessions.

     Statistic tools
     To process the data and to answer the above questions we used several statistical tests:

  • Kruskall-Wallis H test (1952) detects if n data groups belong or not to the same population. This statistic is a non-parametric test, suitable to no-normal distributions such as the exponential distributions observed in web log analysis.
  • Dunn’s post test (1961) compares the difference in the sum of ranks between two columns with the expected average difference (based on the number of groups and their size). It is used after to apply the Kruskall-Wallis or Friedman test. The Dunn’s test shows what samples are different.


     Session length
     We defined a k-session as the number of k access performed by a unique user in a time interval of >30 seconds, between one access and other.

Figure 1. Distribution of observed and calculated sessions by length

     Figure 1 shows the observed and calculated number of sessions according to the length of each session. Thus, we observed 3586 (25,3%) 2-sessions, 3188 (22,5%) 3-sessions, 2227 (15,7%) 4-sessions and so on. We also appreciated that the length distribution of sessions follows an exponential decay (Bianco et al, 2005) similar to the observed in other longitudinal web phenomena (Ortega et al, 2009). The fit of the distribution is high (R2=.98), so we may estimate the number of sessions by length not identified. We may hence estimate that there are 388 10-sessions and 290 11-sessions.
     We introduce a new indicator, half-length, which allows us to detect the median value in non-parametric distributions. It shows what n-sessions are the most frequent ones and the maximum number of clicks is needed to request the information demanded in the website.
     Mathematically the half length is expressed as:

where the half-length l1/2 is the natural logarithm of 2 divided by the decay constant (lamda) and is found from the exponential regression:

where Wa is the number of Web pages which have the length a and W1 is the number of 1-sessions.

     In our sample the half-length or median is 2.38, so we can argue that more than the half of the sessions has a length 2, if one surfs through the webometrics.info website. This low length is because this website is a ranking of universities and its information is displayed through tables which are accessible with just two clicks. We think that this indicator makes possible to characterize the length of the sessions and detect if the browsing of a website is fast or slow, their contents can be quickly located or otherwise it has a winding architecture with makes contents hardly accessible.

Figure 2. Marginal distribution of sessions by length

     We intend to see if the length of a session is related to the referrer of each session or the length is independent of the place from the user comes. As we see before, the sessions have been classified in three classes: sessions from a search engine, from a link in other website and from the root URL typed in the web browser. The Figure 2 and Table 2 show slight differences between the three classes of sessions, being the sessions from search engines referrer those with the largest mean (4.163) and the sessions from the root the ones with the smallest mean (4.069). The half-length or median shows that the first half of the link sessions has a length lesser than 2.42 clicks, while the root half has a length lesser than 2.33 clicks. To detect if these differences between the marginal distributions are or not statistically significant, we have used the Kruskall-Wallis test.

K (Observed value) 6.314
K (Critical value) 5.991
DF 2
p-value (Two-tail) 0.043
Alpha) 0.05

Sample Frequency Mean Median (half-length) Standard deviation Groups












Search engine






Table 2. Kruskal-Wallis test with the Dunn’s post test

     Table 2 shows that there are significant differences between the three access methods in the web navigation (p-value=.043; a= .05). So we can assume that the referrer may be an important factor in determining the number of clicks that a user does in a navigation session. The bilateral tests between the three types of session show that those differences are significant between the session from search engines and the session from the root. However, there is no difference between sessions from links and the other two ones.

     Session length

     The duration of the web sessions was defined as the total time performed by a unique user during the website navigation in which a time interval of = 30 seconds was carried out between one access and other.

Figure 3. Sessions frequency distribution by duration (sec.), (log-log).

     Figure 3 shows the distribution of sessions by their duration in seconds. It follows an exponential model with a high fit (R2= .94).The equation that better describe the session duration is:

     Where Si is the number of sessions that last i seconds, S1 the number of sessions that last 1 second and lamda is the decay constant (lamda=.99). This good fit allows us to estimate how many sessions last i seconds. For example, this model estimates 2074 sessions with duration of 30 sec., 3609 ones with 60 sec., and 5584 ones with 120 sec. If we interprete it from a cumulative point of view, 14.66% of the sessions have a duration of less or equal than 30 sec., 25.51% ones are less or equal than 60 sec., and 39.47% ones are less or equal than 120 sec. In our sample, we found that the most frequent session duration is 10 sec., while the 50% of the sessions have a duration higher than 2.2 min.

Figure 4. Marginal distribution of sessions by duration, grouped by type of access

Duration Frequency R2 S1 lamda
Search engine















Table 3. Fit and coefficients of the three types of distribution

     Figure 3 shows the frequency distribution of the three classes of sessions according to their access point (search engine, links and root), which also follow an exponential decay. We observe that the sessions from search engines are those with the largest duration distribution, followed by Links and Root. Table 4 shows the principal parameters of the three distributions. The fit in the three distributions is high, although it decreases due to the sample size. It is interesting to notice that the decay constant (lamda) remain roughly the same in the three samples (˜.99). We may estimate that the 13.41% of the sessions from a search engines have duration of less or equal than 30 sec., while the percentage of sessions less or equal than 30 sec. is 17.66% for session from links and 26.33% for sessions from the root.

K (Observed value) 234.728
K (Critical value) 5.991
DF 2
p-value (Two-tail) <0.0001
Alpha) 0.05

Sample Frequency Mean Median (half-length) Standard deviation Groups












Search engine






Table 4. Kruskal-Wallis test with the Dunn’s post test

     Table 4 shows the Kruskal-Wallis test for differences in non-parametric sample distributions. Dunn’s post test confirms the significant bilateral differences between the three samples. So we can state that the duration of the sessions is different according to the referrer of each session. Being the users from the search engines who most time pass in the website (median=148 sec.), followed by link’s users (median= 108 sec.) and root’s users (median= 92 sec.).


     When a web log analysis is done, one of the principal limitations is the dificulty to generalize the results and put it in context with other similar results. Most of them may be affected by the own architecture of the website, the contents that it offers and the navigational habits of their visitors. The architecture may determinate the lenght of a session and the contents the time that a user spends in the website. Thus if we compare our results with the Markov and Larose’s (2007) ones, we observe that the median length of the webometrics.info sessions (2.38) is higher than the CCSU website (1), but lower than the EPA website (3). These differences may depend of the number of pages that each website hosts and how those pages are organized. In our case, 2.38 clicks is a high value because webometrics.info is a reference website that needs just two clicks to access to the demanded information. We think that it is possible that users explore different regional rankings or check the position of different universities in those rankings. However, if we compare the median duration of the webometrics.info sessions (132 sec.) with the CCSU (301 sec.) and EPA (317 sec.) websites, our result is almost three times lower. We think that this due to the referential aspect of webometrics.info, where users just check the position of a university in the ranking.
     This paper has shown there are statistical differences between the sessions originated from a search engine, a link or from the root. Thus, visitors from search engines spend more time and their sessions are larger than the visitors that come from a link or typing the URL in their browsers. This may be because the search engines’s users have already defined their information needs they are looking for webometrics or something about university rankings, while the link’s user may arrive to the website surfing in the web with no clear information seek. These results confirm the success of the search engines as navigational agents (Lavene, 2005), because the users may prefer to search relevant web sites in a search engine rather than type the url in the browser, which would explain the differences between search engine’s users and web site root’s users. Moreover, if these results could be extensive to other websites, we may draw the conclusion that the web managers have to give more importance to the visibility and position of their websites in search engines, through a good Search Engine Optimization (SEO) policy (Sen, 2005), because the visits from that point may be consider the most important. Anyway, forthcoming studies were will welcome to explorer these differences in different websites and to observe if these patterns are general or only are a particular characteristic of


     The obtained results allow us to solve the raised questions. The frequency distribution of the session length by their number of click follows an exponential decay which has made possible to estimate how many sessions there are with n length. Similar trend has been observed with the session duration which allows us to estimate how many sessions of a certain time are spent in our website. The description of these distributions may help to the website monitoring, the comparison with other websites and to know the behaviour of our visitors, improving the web pages design and our services.
     Web log analysis has found statistical differences between the originated sessions from different point of access. Thus, sessions coming from search engines are longer in number of clicks than the sessions from the web site root. Similar result is found with regard the spent time by a user that come from a search engine, a link or the root. Search engine’s users devote more time to our web site that user coming from other point of access.
     These results allow us to state that the search engines’ visitors spend more time and hit more pages than the users from other points of access. We may then conclude that a good SEO policy would be justified because the most relevant visits are those coming from the search engines.


Anick, P. (2003). Using terminological feedback for web search refinement: a log-based study. In: Proceedings of the 26th Annual international ACM SIGIR Conference on Research and Development in informaion Retrieval, Toronto, Canada,

Bianco, A., Mardente, G., Mellia, M., Munafo, M. & Muscariello, L. (2005). Web user session characterization via clustering techniques. In: Global Telecommunications Conference, 2005. GLOBECOM '05. IEEE,

Catledge, L., & Pitkow, J. (1995). Characterizing browsing behaviors on the World Wide Web, Computer Networks and ISDN Systems, 27(6): 1065-1073

Cooley, R., Mobasher, B., & Srivastava, J. (1999). Data Preparation for Mining World Wide Web Browsing Pattern, Knowledge and Information Systems, 1(1):5-32

Cooley, R., Mobasher, B., & Srivastava, J. (1997). Web Mining: Information and Pattern Discovery on the World Wide Web. In: Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence, Newport Beach, CA

Deshpande, M. & Karypis, G. (2004). Selective Markov Models for Predicting Web Page Accesses, ACM Transactions on Internet Technology, 4(2): 163-184.

Dunn, O. J. (1961). Multiple comparisons among means, Journal of the American Statistical Association, 56: 54-64.

Gomory, S., Hoch, R., Lee, J., Podlaseck, M. & Schonberg, E. (1999). Analysis and Visualization of Metrics for Online Merchandizing. In: WebKDD, Springer, San Diego, CA,

He, D., Goker, A. & Harper, D. J. (2002). Combining evidence for automatic Web session identification, Information Processing & Management, 38(5): 727-742

Hochheiser, H. & Shneiderman, B. (1999). Understanding Patterns of User Visits to Web Sites: Interactive Starfield Visualization of WWW Log Data. Institute for Technical Research: College Park, Maryland, US

Huang, X. , Peng, F., An, A. & Schuurmans, D. (2004). Dynamic Web log session identification with statistical language models, Journal of the American Society for Information Science and Technology, 55(14): 1290-1303

Kruskal, W.H. & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis, Journal of the American Statistical Association, 47(260): 583-621.

Kurth, M. (1993). The limits and limitations of transaction log analysis, Library Hi Tech, 42: 98-104.

Jansen, B. J. & Spink, A. (2006). How are we searching the World Wide Web? A comparison of nine search engine transaction logs, Information Processing & Management, 42(1):248-263,

Jansen, B. J., Spink, A. & Pederson, J. (2005). A Temporal Comparison of AltaVista Web Searching, Journal of the American Society for Information Science and Technology, 56(6): 559-570

Lam, H., Russell, D., Tang, D. & Munzner, T. (2007). Session Viewer: Visual Exploratory Analysis of Web Session Logs. In: IEEE Symposium on Visual Analytics Science and Technology, Sacramento, CA,

Lavene, M. (2005). An Introduction to Search Engines and Web Navigation, Pearson Education, London

Markov, Z. & Larose, D. T. (2007). Exploratory Data Analysis for Web Usage Mining. In: Z. Markov, D. T. Larose (Eds.) Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage, Wiley

Nasraoui, O., Krishnapuram, R. & Joshi, A. (1999). Mining Web Access Logs Using Relational competetive Fuzzy Clustering. In: 8th International Fuzzy Systems Association World Congress, Taipei, Taiwan

Ortega, J. L., Cothey, V. & Aguillo, I. F. (2009). How old is the Web? Characterizing the age and the currency of the European scientific Web, Scientometrics, 81(1): 295-309

Peters, T.A. (1993). The history and development of transaction log analysis, Library Hi Tech, 42: 41-66.

Pitkow, J. (1997). In search of reliable usage data on the WWW. In: Sixth International World Wide Web Conference, Santa Clara, CA, 451-463

Sen, R. (2005). Optimal Search Engine Marketing Strategy, International Journal of Electronic Commerce, 10(1):9-25

Silverstein, C., Henzinger, M., Marais, H. & Moricz, M. (1998). Analysis of a Very Large AltaVista Query Log, SRC Technical note #1998-14. http://citeseer.ist.psu.edu/70663.html

Silverstein, C., Marais, H., Henzinger, M. & Moricz, M. (1999). Analysis of a very large web search engine query log, ACM SIGIR Forum, 33(1): 6 -12

Spiliopoulou, M. (2000). Web Usage Mining for Web Site Evaluation. Communications of the ACM, 43(8)

Thelwall, M. (2001). Web log file analysis: Backlinks and queries, ASLIB Proceedings, 53: 217-223.

Teevan, J., Adar, E., Jones, R., & Potts, M. (2006). History repeats itself: repeat queries in Yahoo's logs. In: Proceedings of the 29th Annual international ACM SIGIR Conference on Research and Development in information Retrieval, Seattle, USA,

Wang, W. & Zaļane, O. R. (2002). Clustering Web Sessions by Sequence Alignment. In: Proceedings of the 13th international Workshop on Database and Expert Systems Applications, Washington, USA,