Search (135 results, page 1 of 7)

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Abstract

A common problem encountered in Web improvement is how to arrange the homepage links of a Website. This study analyses Web information search behavior, and applies the multiple demand theory to propose two models to help a visitor allocate time for multiple links. The process of searching is viewed as a formal choice problem in which the visitor attempts to choose from multiple Web links to maximize the total utility. The proposed models are calibrated to clickstream data collected from an educational institute over a seven-and-a-half month period. Based on the best fit model, a metric, utility loss, is constructed to measure the performance of each link and arrange them accordingly. Empirical results show that the proposed metric is highly efficient for prioritizing the links on a homepage and the methodology can also be used to study the feasibility of introducing a new function in a Website.

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Abstract

A half of online display ads are not rendered viewable because the users do not scroll deep enough or spend sufficient time at the page depth where the ads are placed. In order to increase the marketing efficiency and ad effectiveness, there is a strong demand for viewability prediction from both advertisers and publishers. This paper aims to predict the dwell time for a given urn:x-wiley:23301635:media:asi24025:asi24025-math-0001 triplet based on historic data collected by publishers. This problem is difficult because of user behavior variability and data sparsity. To solve it, we propose predictive models based on Factorization Machines and Field-aware Factorization Machines in order to overcome the data sparsity issue and provide flexibility to add auxiliary information such as the visible area of a user's browser. In addition, we leverage the prior dwell time behavior of the user within the current page view, that is, time series information, to further improve the proposed models. Experimental results using data from a large web publisher demonstrate that the proposed models outperform comparison models. Also, the results show that adding time series information further improves the performance.

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Date

19. 1.2019 17:42:22

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Abstract

Hundreds of scholarly studies have investigated various aspects of Wikipedia. Although a number of literature reviews have provided overviews of this vast body of research, none has specifically focused on the readers of Wikipedia and issues concerning its readership. In this systematic literature review, we review 99 studies to synthesize current knowledge regarding the readership of Wikipedia and provide an analysis of research methods employed. The scholarly research has found that Wikipedia is popular not only for lighter topics such as entertainment but also for more serious topics such as health and legal information. Scholars, librarians, and students are common users, and Wikipedia provides a unique opportunity for educating students in digital literacy. We conclude with a summary of key findings, implications for researchers, and implications for the Wikipedia community.

Date

18.11.2014 13:22:03

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Abstract

Political sentiment analysis using social media, especially Twitter, has attracted wide interest in recent years. In such research, opinions about politicians are typically divided into positive, negative, or neutral. In our research, the goal is to mine political opinion from social media at a higher resolution by assessing statements of opinion related to the personality traits of politicians; this is an angle that has not yet been considered in social media research. A second goal is to contribute a novel retrieval-based approach for tracking public perception of personality using Gough and Heilbrun's Adjective Check List (ACL) of 110 terms describing key traits. This is in contrast to the typical lexical and machine-learning approaches used in sentiment analysis. High-precision search templates developed from the ACL were run on an 18-month span of Twitter posts mentioning Obama and Romney and these retrieved more than half a million tweets. For example, the results indicated that Romney was perceived as more of an achiever and Obama was perceived as somewhat more friendly. The traits were also aggregated into 14 broad personality dimensions. For example, Obama rated far higher than Romney on the Moderation dimension and lower on the Machiavellianism dimension. The temporal variability of such perceptions was explored.

Date

22. 1.2016 11:25:47

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Abstract

User-generated content is one of the most interesting phenomena of current published media, as users are now able not only to consume, but also to produce content in a much faster and easier manner. However, such freedom also carries concerns about content quality. In this work, we propose an automatic framework to assess the quality of collaboratively generated content. Quality is addressed as a multidimensional concept, modeled as a combination of independent assessments, each regarding different quality dimensions. Accordingly, we adopt a machine-learning (ML)-based multiview approach to assess content quality. We perform a thorough analysis of our framework on two different domains: Questions and Answer Forums and Collaborative Encyclopedias. This allowed us to better understand when and how the proposed multiview approach is able to provide accurate quality assessments. Our main contributions are: (a) a general ML multiview framework that takes advantage of different views of quality indicators; (b) the improvement (up to 30%) in quality assessment over the best state-of-the-art baseline methods; (c) a thorough feature and view analysis regarding impact, informativeness, and correlation, based on two distinct domains.

Date

16.11.2017 13:04:22

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Abstract

This paper introduces a project to develop a reliable, cost-effective method for classifying Internet texts into register categories, and apply that approach to the analysis of a large corpus of web documents. To date, the project has proceeded in 2 key phases. First, we developed a bottom-up method for web register classification, asking end users of the web to utilize a decision-tree survey to code relevant situational characteristics of web documents, resulting in a bottom-up identification of register and subregister categories. We present details regarding the development and testing of this method through a series of 10 pilot studies. Then, in the second phase of our project we applied this procedure to a corpus of 53,000 web documents. An analysis of the results demonstrates the effectiveness of these methods for web register classification and provides a preliminary description of the types and distribution of registers on the web.

Date

4. 8.2015 19:22:04

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Abstract

Purpose - The purpose of this paper is to engage in a systematic analysis of academic research that relies on the collection and use of Twitter data, creating topology of Twitter research that details the disciplines and methods of analysis, amount of tweets and users under analysis, the methods used to collect Twitter data, and accounts of ethical considerations related to these projects. Design/methodology/approach - Content analysis of 382 academic publications from 2006 to 2012 that used Twitter as their primary platform for data collection and analysis. Findings - The analysis of over 380 scholarly publications utilizing Twitter data reveals noteworthy trends related to the growth of Twitter-based research overall, the disciplines engaged in such research, the methods of acquiring Twitter data for analysis, and emerging ethical considerations of such research. Research limitations/implications - The findings provide a benchmark analysis that must be updated with the continued growth of Twitter-based research. Originality/value - The research is the first full-text systematic analysis of Twitter-based research projects, focussing on the growth in discipline and methods as well as its ethical implications. It is of value for the broader research community currently engaged in social media-based research, and will prompt reflexive evaluation of what research is occurring, how it is occurring, what is being done with Twitter data, and how researchers are addressing the ethics of Twitter-based research.

Date

20. 1.2015 18:30:22

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Abstract

Online discussion forums have become a popular medium for users to discuss with and seek information from other users having similar interests. A typical discussion thread consists of a sequence of posts posted by multiple users. Each post in a thread serves a different purpose providing different types of information and, thus, may not be equally useful for all applications. Identifying the purpose and nature of each post in a discussion thread is thus an interesting research problem as it can help in improving information extraction and intelligent assistance techniques. We study the problem of classifying a given post as per its purpose in the discussion thread and employ features based on the post's content, structure of the thread, behavior of the participating users, and sentiment analysis of the post's content. We evaluate our approach on two forum data sets belonging to different genres and achieve strong classification performance. We also analyze the relative importance of different features used for the post classification task. Next, as a use case, we describe how the post class information can help in thread retrieval by incorporating this information in a state-of-the-art thread retrieval model.

Date

22. 1.2016 11:50:46

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Abstract

Purpose - This paper is the final one in a series which has tried to give an overview of so-called transformational areas of digital library technology. The aim has been to assess how much real transformation these applications can bring about, in terms of creating genuine user benefit and also changing everyday library practice. Design/methodology/approach - The paper provides a summary of some of the legal and ethical issues associated with web 2.0 applications in libraries, associated with a brief retrospective view of some relevant literature. Findings - Although web 2.0 innovations have had a massive impact on the larger World Wide Web, the practical impact on library service delivery has been limited to date. What probably can be termed transformational in the effect of web 2.0 developments on library and information work is their effect on some underlying principles of professional practice. Research limitations/implications - The legal and ethical challenges of incorporating web 2.0 platforms into mainstream institutional service delivery need to be subject to further research, so that the risks associated with these innovations are better understood at the strategic and policy-making level. Practical implications - This paper makes some recommendations about new principles of library and information practice which will help practitioners make better sense of these innovations in their overall information environment. Social implications - The paper puts in context some of the more problematic social impacts of web 2.0 innovations, without denying the undeniable positive contribution of social networking to the sphere of human interactivity. Originality/value - This paper raises some cautionary points about web 2.0 applications without adopting a precautionary approach of total prohibition. However, none of the suggestions or analysis in this piece should be considered to constitute legal advice. If such advice is required, the reader should consult appropriate legal professionals.

Date

22. 1.2011 17:54:04

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Abstract

Purpose The purpose of this paper is to investigate the relationship between internet filtering, emotions and information-seeking behaviour. Design/methodology/approach In total, 15 postgraduate students at an Iranian university participated in the study which involved a questionnaire, search tasks with think aloud narratives, and interviews. Findings Internet content filtering results in some changes in the information-seeking behaviour of users. Users who face website blocking use a variety of methods to bypass filtering, mostly by using anti-filter software. Filtering encourages users to use channels such as social networking services to share resources and it increases the use of library material by some of the users. Users who face filtering during their search are more likely to visit more pages of results and click on more hits in the results, unlike users who do not experience filtering who rarely go past the first page. Blocking users' access to content stimulates their curiosity and they become more determined to access the content. In terms of the affective aspect, filtering causes several negative emotions (e.g. anger, disgust, sadness and anxiety) and the main reason for these emotions is not the inability to access information but the feeling of being controlled and not having freedom. Research limitations/implications The study was limited to a small number of postgraduate students in social sciences and not generalisable to all user groups. The implication is that in countries where filtering is used, libraries can play an important role in serving users and reducing users negative emotions, especially if libraries can take advantage of technologies such as social media for their services. Originality/value This is first study to address the effects of internet filtering on information-seeking behaviour and emotions. The study shows that internet filtering causes negative emotions and results in some changes in information-seeking behaviour.

Date

20. 1.2015 18:30:22

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Abstract

Webcasting systems were developed to provide remote access in real-time to live events. Today, these systems have an additional requirement: to accommodate the "second life" of webcasts as archival information objects. Research to date has focused on facilitating the production and storage of webcasts as well as the development of more interactive and collaborative multimedia tools to support the event, but research has not examined how people interact with a webcasting system to access and use the contents of those archived events. Using an experimental design, this study examined how 16 typical users interact with a webcasting system to respond to a set of information tasks: selecting a webcast, searching for specific information, and making a gist of a webcast. Using several data sources that included user actions, user perceptions, and user explanations of their actions and decisions, the study also examined the strategies employed to complete the tasks. The results revealed distinctive system-use patterns for each task and provided insights into the types of tools needed to make webcasting systems better suited for also using the webcasts as information objects.

Date

22. 1.2011 14:16:14

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Abstract

Purpose - The purpose of this paper is to get a first approximation of the usefulness of online forums with regard to information seeking and knowledge generation. Design/methodology/approach - This study captures the characteristics of knowledge generation by examining the pragmatics and types of information needs of posted questions and by investigating knowledge related characteristics of discussion posts as well as the success of communication. Three online forums were examined. The data set consists of 55 threads, containing 533 posts which were categorized manually by two researchers. Findings - Results show that questioners often ask for personal estimations. Information needs often aim for actionable insights or uncertainty reduction. With regard to answers, factual information is the dominant content type and has the highest knowledge value as it is the strongest predictor with regard to the generation of new knowledge. Opinions are also relevant, but in a rather subsequent and complementary way. Emotional aspects are scarcely observed. Overall, results indicate that knowledge creation predominantly follows a socio-cultural paradigm of knowledge exchange. Research limitations/implications - Although the investigation captures important aspects of knowledge building processes, the measurement of the forums' knowledge value is still rather limited. Success is only partly measurable with the current scheme. The central coding category "new topical knowledge" is only of nominal value and therefore not able to compare different kinds of knowledge gains in the course of discussion. Originality/value - The investigation reaches out beyond studies that do not consider that the role and relevance of posts is dependent on the state of the discussion. Furthermore, the paper integrates two perspectives of knowledge value: the success of the questioner with regard to the expressed information need and the knowledge building value for communicants and readers.

Date

20. 1.2015 18:30:22

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Abstract

The goal of this research is to evaluate the effect of ad rank on the performance of keyword advertising campaigns. We examined a large-scale data file comprised of nearly 7,000,000 records spanning 33 consecutive months of a major US retailer's search engine marketing campaign. The theoretical foundation is serial position effect to explain searcher behavior when interacting with ranked ad listings. We control for temporal effects and use one-way analysis of variance (ANOVA) with Tamhane's T2 tests to examine the effect of ad rank on critical keyword advertising metrics, including clicks, cost-per-click, sales revenue, orders, items sold, and advertising return on investment. Our findings show significant ad rank effect on most of those metrics, although less effect on conversion rates. A primacy effect was found on both clicks and sales, indicating a general compelling performance of top-ranked ads listed on the first results page. Conversion rates, on the other hand, follow a relatively stable distribution except for the top 2 ads, which had significantly higher conversion rates. However, examining conversion potential (the effect of both clicks and conversion rate), we show that ad rank has a significant effect on the performance of keyword advertising campaigns. Conversion potential is a more accurate measure of the impact of an ad's position. In fact, the first ad position generates about 80% of the total profits, after controlling for advertising costs. In addition to providing theoretical grounding, the research results reported in this paper are beneficial to companies using search engine marketing as they strive to design more effective advertising campaigns.

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Abstract

This paper discusses an approach of collecting qualitative data on human information behaviour that is based on mining web data using search engines. The approach is technically the same that has been used for some time in webometric research to make statistical inferences on web data, but the present paper shows how the same tools and data collecting methods can be used to gather data for qualitative data analysis on human information behaviour.

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Abstract

Academic social networking sites seek to bring the benefits of online networking to an academic audience. The ability to make connections to others is a defining characteristic of the sites, but what types of networks are formed, and what are the implications of the structures? This study addressed that question through mixed methods social network analysis, focusing on Academia.edu, ResearchGate, and Twitter, as three of the main sites used by academics in their professional lives. The structure of academics' ego-networks on social networking sites differs by platform. Networks on academic sites were smaller and more highly clustered, whereas Twitter networks were larger and more diffuse. Institutions and research interests define communities on academic sites, compared with research topics and personal interests on Twitter. The network structures reflect differences in how academics conceptualize different sites and have implications in relation to fostering social capital and research impact.

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Date

22. 6.2018 14:22:02

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Abstract

Disclosure of personal information on social networks has been extensively researched in recent years from different perspectives, including the influence of demographic, personality, and social parameters on the extent and type of disclosure. However, although some of the most widespread uses of these networks nowadays are for professional, academic, and business purposes, a thorough investigation of professional information disclosure is still needed. This study's primary aim, therefore, is to conduct a systematic and comprehensive investigation into patterns of professional information disclosure and various factors involved on different types of social networks. To this end, a user survey was conducted. We focused specifically on Facebook and LinkedIn, the 2 diverse networks most widely used in Israel. Significant differences were found between these networks. For example, we found that on Facebook professional pride is a factor in professional information disclosure, whereas on LinkedIn, work seniority and income have a significant effect. Thus, our findings shed light on the attitudes and professional behavior of network members, leading to recommendations regarding advertising strategies and network-appropriate self-presentation, as well as approaches that companies might adopt according to the type of manpower required.

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Abstract

YouTube is a successful social network that people use to upload, watch, and comment on videos. We believe comments left on these videos can provide insight into user interests, but to this point have not been used to map out a specific video community. Our study investigates whether and how user commenting behavior impacts the topology of the K-pop video community through analysis of co-commenting behavior on these videos. We apply a traditional author cocitation analysis to this behavior, in a process we refer to as co-comment analysis, to detect the topology of this community. This involves: a) an analysis of user co-comments to elicit the inclination of user homophily within the community; b) an analysis of user co-comments, weighted frequency of co-comments, to detect user interests in the community; and c) an analysis of user co-comments, weighted sentiment scores, to capture user opinions by polarity. The results indicate that users who comment on specific K-pop videos also tend to comment on topically similar YouTube videos. We also find that the number of comments made by users correlates with the degree of positivity of their comments. Conversely, users who comment negatively on K-pop videos are not inclined to form specific user groups, but rather present only their opinions individually.

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Abstract

In recent years we have witnessed a significant growth of social-computing communities - online services in which users share information in various forms. As content contributions from participants are critical to the viability of these communities, it is important to understand what drives users to participate and share information with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with various forms of participation in a large online photo-sharing community. Using survey and system data, we examine four different forms of participation and consider the differences between these forms. We build on theories of motivation to examine the relationship between users' participation and their motivations with respect to their tenure in the community. Amongst our findings, we identify individual motivations (both extrinsic and intrinsic) that underpin user participation, and their effects on different forms of information sharing; we show that tenure in the community does affect participation, but that this effect depends on the type of participation activity. Finally, we demonstrate that tenure in the community has a weak moderating effect on a number of motivations with regard to their effect on participation. Directions for future research, as well as implications for theory and practice, are discussed.