Who visits academic institutional repositories and why?: An analytical approach for a single high profile repository

<p>Institutional Repositories have been set up all over the world, and are now mainstream business for academic libraries and other organisations. The nature of the visitors or users for these repositories is not well understood, and little work has been done in analysing the data the repositories generate on their visitors. This report looks at the analytics generated by the University of Canterbury Research Repository (UCRR) through its own internal statistics and Google Analytics. There are many issues with reconciling this data, as many factors influence the accuracy of the figures, including web search engine crawlers, deep linking, and copyright trolls. This report found that there are many visitors to the UCRR, and that it is difficult, but possible to create narratives for specific items indicating how they might be used. Generalisations, however are much harder to make, and though we can see who is visiting the UCRR, we cannot really ascertain why they do. This report provides suggestions for further research on repository users, particularly at gathering qualitative data from groups identified from this quantative analytics.</p>

Who visits academic institutional repositories and why?: An analytical approach for a single high profile repository

<p>Institutional Repositories have been set up all over the world, and are now mainstream business for academic libraries and other organisations. The nature of the visitors or users for these repositories is not well understood, and little work has been done in analysing the data the repositories generate on their visitors. This report looks at the analytics generated by the University of Canterbury Research Repository (UCRR) through its own internal statistics and Google Analytics. There are many issues with reconciling this data, as many factors influence the accuracy of the figures, including web search engine crawlers, deep linking, and copyright trolls. This report found that there are many visitors to the UCRR, and that it is difficult, but possible to create narratives for specific items indicating how they might be used. Generalisations, however are much harder to make, and though we can see who is visiting the UCRR, we cannot really ascertain why they do. This report provides suggestions for further research on repository users, particularly at gathering qualitative data from groups identified from this quantative analytics.</p>

QoS Based Ranking For Web Search

Text, link and usage information are the most commonly used sources in the ranking algorithm of a web search engine. In this thesis, we argue that the quality of the web pages such as the performance of the page delivery (e.g. reliability and response time) should also play an important role in ranking, especially for users with a slow Internet connection or mobile users. Based on this principle, if two pages have the same level of relevancy to a query, the one with a higher delivery quality (e.g. faster response) should be ranked higher. We define several important attributes for the Quality of Service (QoS) and explain how we rank the web pages based on these algorithms. In addition, while combining those QoS attributes, we have tested and compared different aggregation algorithms. The experiment results show that our proposed algorithms can promote the pages with a higher delivery quality to higher positions in the result list, which is beneficial to users to improve their overall experiences of using the search engine and QoS based re-ranking algorithm always gets the best performance.

QoS Based Ranking For Web Search

Text, link and usage information are the most commonly used sources in the ranking algorithm of a web search engine. In this thesis, we argue that the quality of the web pages such as the performance of the page delivery (e.g. reliability and response time) should also play an important role in ranking, especially for users with a slow Internet connection or mobile users. Based on this principle, if two pages have the same level of relevancy to a query, the one with a higher delivery quality (e.g. faster response) should be ranked higher. We define several important attributes for the Quality of Service (QoS) and explain how we rank the web pages based on these algorithms. In addition, while combining those QoS attributes, we have tested and compared different aggregation algorithms. The experiment results show that our proposed algorithms can promote the pages with a higher delivery quality to higher positions in the result list, which is beneficial to users to improve their overall experiences of using the search engine and QoS based re-ranking algorithm always gets the best performance.

Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues.

Tren Penggunaan Framework COBIT, ITIL, dan ISO 27001 Pada Rentang Tahun 2014-2018 di Indonesia

The use of Information Technology (IT) frameworks from year to year in Indonesia has shown an increase. However, there is not much research on the use of IT frameworks. The research on IT frameworks can be used by a company to analyze trends in the use of frameworks in Indonesia. That way, it can help a company consider choosing a framework to use. The IT frameworks discussed in this study are COBIT, ITIL, and ISO 27001. Data related to research IT frameworks taken in the 2014-2018 period. Data collection through the Google Scholar web search engine using the Publish or Perish software. The data obtained were analyzed using descriptive quantitative methods. The results of this study can use to determine the use of the IT framework in Indonesia. The amount of raw data obtained from the data collection process was 5,708 data with Authors, Title, Years, and Publisher variables. Research data on titles and keywords obtained in the Indonesian domain amounted to 754 and overseas amounted to 194. From the research results, it can conclude that the most widely used framework is the COBIT framework, with a percentage of 69.3% in the Indonesian and 58.7% in the foreign domain.

“Outside the industry, nobody knows what we do” SEO as seen by search engine optimizers and content providers

PurposeIn commercial web search engine results rankings, four stakeholder groups are involved: search engine providers, users, content providers and search engine optimizers. Search engine optimization (SEO) is a multi-billion-dollar industry and responsible for making content visible through search engines. Despite this importance, little is known about its role in the interaction of the stakeholder groups.Design/methodology/approachWe conducted expert interviews with 15 German search engine optimizers and content providers, the latter represented by content managers and online journalists. The interviewees were asked about their perspectives on SEO and how they assess the views of users about SEO.FindingsSEO was considered necessary for content providers to ensure visibility, which is why dependencies between both stakeholder groups have evolved. Despite its importance, SEO was seen as largely unknown to users. Therefore, it is assumed that users cannot realistically assess the impact SEO has and that user opinions about SEO depend heavily on their knowledge of the topic.Originality/valueThis study investigated search engine optimization from the perspective of those involved in the optimization business: content providers, online journalists and search engine optimization professionals. The study therefore contributes to a more nuanced view on and a deeper understanding of the SEO domain.