Event-based collections are often started with a web search, but the search results you find on Day 1 may not be the same as those you find on Day 7. In this paper, we consider collections that originate from extracting URIs (Uniform Resource Identifiers) from Search Engine Result Pages (SERPs). Specifically, we seek to provide insight about the retrievability of URIs of news stories found on Google, and to answer two main questions: first, can one "refind" the same URI of a news story (for the same query) from Google after a given time? Second, what is the probability of finding a story on Google over a given period of time? To answer these questions, we issued seven queries to Google every day for over seven months (2017-05-25 to 2018-01-12) and collected links from the first five SERPs to generate seven collections for each query. The queries represent public interest stories: "healthcare bill," "manchester bombing," "london terrorism," "trump russia," "travel ban," "hurricane harvey," and "hurricane irma." We tracked each URI in all collections over time to estimate the discoverability of URIs from the first five SERPs. Our results showed that the daily average rate at which stories were replaced on the default Google SERP ranged from 0.21 - 0.54, and a weekly rate of 0.39 - 0.79, suggesting the fast replacement of older stories by newer stories. The probability of finding the same URI of a news story after one day from the initial appearance on the SERP ranged from 0.34 - 0.44. After a week, the probability of finding the same news stories diminishes rapidly to 0.01 - 0.11. In addition to the reporting of these probabilities, we also provide two predictive models for estimating the probability of finding the URI of an arbitrary news story on SERPs as a function of time. The web archiving community considers link rot and content drift important reasons for collection building. Similarly, our findings suggest that due to the difficulty in retrieving the URIs of news stories from Google, collection building that originates from search engines should begin as soon as possible in order to capture the first stages of events, and should persist in order to capture the evolution of the events, because it becomes more difficult to find the same news stories with the same queries on Google, as time progresses.
Cho and Garcia-Molina @cite_13 downloaded 720,000 pages (from 270 web servers) daily for four months to quantify the degree of change of web pages over time. They found that about 40 In the web archiving community, link rot and content drift @cite_22 @cite_20 are two major reasons for collection building. Comparably, the difficultly in refinding news stories on the SERP suggests instant and persistent collection building by efforts that rely on the SERP for seed extraction or collection generation. McCown and Nelson @cite_17 issued queries for five months to search engine web user interfaces (Google, MSN and Yahoo) and their respective APIs, and found significant discrepancies in the results found on both interfaces. They also showed how search results decay over time and modeled the decay rate. Kim and Vitor @cite_1 studied Google, Bing, and Yahoo search engines, and showed that the top 10 results of 90 @cite_9 illustrated the tendency of users to seek to refind web pages previously seen at a rate of about 40
@cite_22 @cite_9 @cite_1 @cite_13 @cite_20 @cite_17
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The emergence of the web has fundamentally affected most aspects of information communication, including scholarly communication. The immediacy that characterizes publishing information to the web, as well as accessing it, allows for a dramatic increase in the speed of dissemination of scholarly knowledge. But, the transition from a paper-based to a web-based scholarly communication system also poses challenges. In this paper, we focus on reference rot, the combination of link rot and content drift to which references to web resources included in Science, Technology, and Medicine (STM) articles are subject. We investigate the extent to which reference rot impacts the ability to revisit the web context that surrounds STM articles some time after their publication. We do so on the basis of a vast collection of articles from three corpora that span publication years 1997 to 2012. For over one million references to web resources extracted from over 3.5 million articles, we determine whether the HTTP URI is still responsive on the live web and whether web archives contain an archived snapshot representative of the state the referenced resource had at the time it was referenced. We observe that the fraction of articles containing references to web resources is growing steadily over time. We find one out of five STM articles suffering from reference rot, meaning it is impossible to revisit the web context that surrounds them some time after their publication. When only considering STM articles that contain references to web resources, this fraction increases to seven out of ten. We suggest that, in order to safeguard the long-term integrity of the web-based scholarly record, robust solutions to combat the reference rot problem are required. In conclusion, we provide a brief insight into the directions that are explored with this regard in the context of the Hiberlink project. People often repeat Web searches, both to find new information on topics they have previously explored and to re-find information they have seen in the past. The query associated with a repeat search may differ from the initial query but can nonetheless lead to clicks on the same results. This paper explores repeat search behavior through the analysis of a one-year Web query log of 114 anonymous users and a separate controlled survey of an additional 119 volunteers. Our study demonstrates that as many as 40 of all queries are re-finding queries. Re-finding appears to be an important behavior for search engines to explicitly support, and we explore how this can be done. We demonstrate that changes to search engine results can hinder re-finding, and provide a way to automatically detect repeat searches and predict repeat clicks. Due to the dynamic nature of web and the complex architectures of modern commercial search engines, top results in major search engines can change dramatically over time. Our experimental data shows that, for all three major search engines (Google, Bing and Yahoo!), approximately 90 of queries have their top 10 results altered within a period of ten days. Although this instability is expected in some situations such as in news-related queries, it is problematic in general because it can dramatically affect retrieval performance measurements and negatively affect users' perception of search quality (for instance, when users cannot re-find a previously found document). In this work we present the first large scale study on the degree and nature of these changes. We introduce several types of query instability, and several metrics to quantify it.We then present a quantitative analysis using 12,600 queries collected from a commercial web search engine over several weeks. Our analysis shows that the results from all major search engines have similar levels of instability, and that many of these changes are temporary. We also identified classes of queries with clearly different instability profiles - for instance, navigational queries are considerably more stable than non-navigational, while longer queries are significantly less stable than shorter ones. In this paper we study how to build an effective incremental crawler. The crawler selectively and incrementally updates its index and or local collection of web pages, instead of periodically refreshing the collection in batch mode. The incremental crawler can improve the freshness'' of the collection significantly and bring in new pages in a more timely manner. We first present results from an experiment conducted on more than half million web pages over 4 months, to estimate how web pages evolve over time. Based on these experimental results, we compare various design choices for an incremental crawler and discuss their trade-offs. We propose an architecture for the incremental crawler, which combines the best design choices. Google, Yahoo and MSN all provide both web user interfaces (WUIs) and application programming interfaces (APIs) to their collections. Whether building collections of resources or studying the search engines themselves, the search engines request that researchers use their APIs and not "scrape" the WUIs. However, anecdotal evidence suggests the interfaces produce different results. We provide the first in depth quantitative analysis of the results produced by the Google, MSN and Yahoo API and WUI interfaces. We have queried both interfaces for five months and found significant discrepancies between the interfaces in several categories. In general, we found MSN to produce the most consistent results between their two interfaces. Our findings suggest that the API indexes are not older, but they are probably smaller for Google and Yahoo. We also examined how search results decay over time and built predictive models based on the observed decay rates. Based on our findings, it can take over a year for half of the top 10 results to a popular query to be replaced in Google and Yahoo; for MSN it may take only 2-3 months.