Search (35 results, page 1 of 2)

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Abstract

Spam and wildly varying documents make searching in Twitter challenging. Most Twitter search systems generally treat a Tweet as a plain text when modeling relevance. However, a series of conventions allows users to Tweet in structural ways using a combination of different blocks of texts. These blocks include plain texts, hashtags, links, mentions, etc. Each block encodes a variety of communicative intent and the sequence of these blocks captures changing discourse. Previous work shows that exploiting the structural information can improve the structured documents (e.g., web pages) retrieval. In this study we utilize the structure of Tweets, induced by these blocks, for Twitter retrieval and Twitter opinion retrieval. For Twitter retrieval, a set of features, derived from the blocks of text and their combinations, is used into a learning-to-rank scenario. We show that structuring Tweets can achieve state-of-the-art performance. Our approach does not rely on social media features, but when we do add this additional information, performance improves significantly. For Twitter opinion retrieval, we explore the question of whether structural information derived from the body of Tweets and opinionatedness ratings of Tweets can improve performance. Experimental results show that retrieval using a novel unsupervised opinionatedness feature based on structuring Tweets achieves comparable performance with a supervised method using manually tagged Tweets. Topic-related specific structured Tweet sets are shown to help with query-dependent opinion retrieval.

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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 - This paper aims to report findings from an exploratory study investigating the web interactions and technoliteracy of children in the early childhood years. Previous research has studied aspects of older children's technoliteracy and web searching; however, few studies have analyzed web search data from children younger than six years of age. Design/methodology/approach - The study explored the Google web searching and technoliteracy of young children who are enrolled in a "preparatory classroom" or kindergarten (the year before young children begin compulsory schooling in Queensland, Australia). Young children were video- and audio-taped while conducting Google web searches in the classroom. The data were qualitatively analysed to understand the young children's web search behaviour. Findings - The findings show that young children engage in complex web searches, including keyword searching and browsing, query formulation and reformulation, relevance judgments, successive searches, information multitasking and collaborative behaviours. The study results provide significant initial insights into young children's web searching and technoliteracy. Practical implications - The use of web search engines by young children is an important research area with implications for educators and web technologies developers. Originality/value - This is the first study of young children's interaction with a web search engine.

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Content

Inhalt: Power, communication and the internet -- The structure and power of search engines -- Google and the politics of online searching -- Users and uses of Google's information -- Mass media channels and the world of Google News -- Google's global mapping

LCSH

Internet searching

Subject

Internet searching

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Abstract

This study focuses on the impact of age, prior domain knowledge and cognitive abilities on performance, query production and navigation strategies during information searching. Twenty older adults and nineteen young adults had to answer 12 information search problems of varying nature within two domain knowledge: health and manga. In each domain, participants had to perform two simple fact-finding problems (keywords provided and answer directly accessible on the search engine results page), two difficult fact-finding problems (keywords had to be inferred) and two open-ended information search problems (multiple answers possible and navigation necessary). Results showed that prior domain knowledge helped older adults improve navigation (i.e. reduced the number of webpages visited and thus decreased the feeling of disorientation), query production and reformulation (i.e. they formulated semantically more specific queries, and they inferred a greater number of new keywords).

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Abstract

We report a naturalistic interactive information retrieval (IIR) study of 18 ordinary users in the age of 20-25 who carry out everyday-life information seeking (ELIS) on the Internet with respect to the three types of information needs identified by Ingwersen (1986): the verificative information need (VIN), the conscious topical information need (CIN), and the muddled topical information need (MIN). The searches took place in the private homes of the users in order to ensure as realistic searching as possible. Ingwersen (1996) associates a given search behaviour to each of the three types of information needs, which are analytically deduced, but not yet empirically tested. Thus the objective of the study is to investigate whether empirical data does, or does not, conform to the predictions derived from the three types of information needs. The main conclusion is that the analytically deduced information search behaviour characteristics by Ingwersen are positively corroborated for this group of test participants who search the Internet as part of ELIS.

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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

S R Ranganathan's ideas have influenced library classification since the inception of his Colon Classification in 1933. His address at Elsinore, "Library Classification Through a Century", was his grand vision of the century of progress in classification from 1876 to 1975, and looked to the future of faceted classification as the means to provide a cohesive system to organize the world's information. Fifty years later, the internet and its achievements, social ecology, and consequences present a far more complicated picture, with the library as he knew it as a very small part and the problems that he confronted now greatly exacerbated. The systematic nature of Ranganathan's canons, principles, postulates, and devices suggest that modern semantic algorithms could guide automatic subject tagging. The vision presented here is one of internet-wide faceted classification and retrieval, implemented as open, distributed facets providing unified faceted searching across all web sites.

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LCSH

Internet searching

Subject

Internet searching

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Abstract

The aim of this study is to understand how teenagers use Internet forums to search for information. The activities of asking for and providing information in a forum were explored, and a set of messages extracted from a French forum targeting adolescents was analyzed. Results show that the messages initiating the threads are often requests for information. Teenagers mainly ask for peers' opinions on personal matters and specific verifiable information. The discussions following these requests take the form of an exchange of advice (question/answer) or a coconstruction of the final answer between the participants (with assessments of participants' responses, requests for explanations, etc.). The results suggest that discussion forums present different advantages for adolescents' information-seeking activities. The first is that this social medium allows finding specialized information on topics specific to this age group. The second is that the collaborative aspect of information seeking in a forum allows these adolescents to overcome difficulties commonly associated with the search process (making a precise request, evaluating a result).

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Abstract

This article explores the need for libraries to algorithmically access and manipulate the world's largest API: the Internet. The billions of pages on the 'Internet API' (HTTP, HTML, CSS, XPath, DOM, etc.) are easily accessible and manipulable. Libraries can assist in creating meaning through the datafication of information on the world wide web. Because most information is created for human consumption, some programming is required for automated extraction. Python is an easy-to-learn programming language with extensive packages and community support for web page automation. Four packages (Urllib, Selenium, BeautifulSoup, Scrapy) in Python can automate almost any web page for all sized projects. An example warrant data project is explained to illustrate how well Python packages can manipulate web pages to create meaning through assembling custom datasets.

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Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.

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Abstract

Given the task of finding influencers (opinion makers) for a given domain in a social network, we investigate (a) what is the relative importance of domain and authority signals, (b) what is the most effective way of combining signals (voting, classification, learning to rank, etc.) and how best to model the vocabulary signal, and (c) how large is the gap between supervised and unsupervised methods and what are the practical consequences. Our best results on the RepLab dataset (which improves the state of the art) uses language models to learn the domain-specific vocabulary used by influencers and combines domain and authority models using a Learning to Rank algorithm. Our experiments show that (a) both authority and domain evidence can be trained from the vocabulary of influencers; (b) once the language of influencers is modeled as a likelihood signal, further supervised learning and additional network-based signals only provide marginal improvements; and (c) the availability of training data sets is crucial to obtain competitive results in the task. Our most remarkable finding is that influencers do use a distinctive vocabulary, which is a more reliable signal than nontextual network indicators such as the number of followers, retweets, and so on.

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Date

22. 6.2018 14:22:02

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Date

22. 6.2018 14:29:46

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Date

19. 1.2019 17:42:22

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Date

22. 1.2016 14:37:14

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Date

3. 5.1997 8:44:22

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Date

22. 1.2011 14:27:06