Blog Search Engines Research Articles

A novel time-shifting method to find popular blog post topics

Blog search engines and general search engines automatically crawl Web pages from the Internet and generate search results to users. One difference between the two is that blog search engines focus on blog posts and filter general pages. This difference allows bloggers to focus only on page results for posts rather than other types of page results. Another difference is that posts involve more time-related issues than general pages. For general pages, the general search engine can only show the last update time for the page. However, for posts, the blog search engine can show all possible time clues for the post. For some frequently updated posts, time clues help bloggers find information more efficiently. In this paper, we first use some well-known semantic analysis models to analyze the performance of Google Blog Search. Next, we apply a hybrid strategy that considers the document link and time clue relationships between posts to further improve its retrieval performance. Finally, we present several experiments to simulate various possible scenarios to confirm the effectiveness of our strategy.

Based on The Document-Link and Time-Clue Relationships Between Blog Posts to Improve the Performance of Google Blog Search

Both the blog search engine and the general search engine automatically crawl the pages from the web and produce relevant search results based on the user's query. The first difference between the two types is that the blog search engine focuses on dealing with blog posts and filters out other types of pages. This difference allows bloggers only to care about posts rather than all pages that are indexed by general search engines. The second difference is the post, considering more time-related issues compared to the page. The semantic analysis model is widely used to analyze the various semantic relationships that may arise in the document. In this article, the authors propose a new semantic analysis model to find possible time relationships between posts. The main contribution of this paper has two points: first is that this paper builds a high-performance search system that considers the discussion topic and updated time between posts; second, is that the authors consider the time relationships between posts that can rank the relevant blog topics based on the popularity of the posts.

A cost-effective solution for blog search

In recent years, blogging research has grown rapidly in social networks and the number of posts has continued to grow. An effective search method for these growing posts is to use a blog search engine to help bloggers quickly and accurately find the information they need. The blog search engine faces an important problem of the short query entered by the user like the general search engine. This problem makes it difficult for search engines to correctly define the nature of user queries. Relevant literature shows that many researchers have tried to solve this problem by using different semantic analysis models. However, these models are not suitable for big data environments such as search engines because they need significant computing time. In this paper, we propose a semantic analysis model with a dynamic judgment mechanism. According to the experimental results, our model can achieve a cost-effective solution in computing time and execution performance. That is, we can use a relatively small amount of computing time to achieve a near-optimal solution performance.

An effective LDA-based time topic model to improve blog search performance

Blog search engines and general search engines automatically crawl web pages from the Internet and produce search results for users. One difference between the two is that blog search engines focus on posts and ignore the rest of the pages. Obviously, the pages indexed by the general search engine are always greater than the posts. This feature allows bloggers to focus only on the posts they are interested in, rather than other types of pages. The other difference is that posts involve more time-related issues compared to general pages. For the general pages, the general search engine often can only show the last update time. However, for the post, the blog search engine can display various possible times. For some often updated posts, the time factor can help bloggers find information more efficiently.In this paper, we first use some well-known semantic analysis models to analyze the performance of the blog search. Next, we consider the time relationship between posts to further improve its performance. Finally, we provide some experiments to simulate various possible scenarios to confirm the effectiveness of this relationship. The contributions of this paper are twofold. One is that we build a high-performance system that considers the importance of blog topics at different times. The other is that we consider the time relationship between posts, which can rank the relevant blog topics based on the popularity of the posts.

A THEMATIC ANALYSIS OF DEMENTIA BLOG NARRATIVES: PERSPECTIVES FROM CAREGIVERS AND CARE RECIPIENTS

Living with a chronic disease, including Alzheimer's disease (AD) and other dementias, can be isolating. Blogging provides opportunities to communicate with others through narratives. The purpose of this study was to analyze blog posts of AD/dementia caregivers and care recipients to increase understanding of their experiences, needs, and perspectives. This information provides valuable insight to better support people affected by AD/dementia. A search was performed using Google, a blog search engine, and lists of award-winning blogs related to AD/dementia. 48 caregiver and 24 care recipient blogs were selected for this study. Blogs written by organizations or healthcare professionals were excluded. The 5 most recent blog posts containing 10+ lines of original content were selected for analysis from each blog. Initially, subsets of 5 blogs from the caregiver group and 5 from the care recipient group were selected for line-by-line open coding and for development of the codebook. Open codes were examined to determine overarching themes in the data using grounded theory. The remaining blogs were coded and organized using NVivo software. Sub-categories were established based on thematic overlap among coded material. Overarching themes included: 1) recognition of the toll of the disease on the caregiver and/or care recipient; 2) seeing the positives of the situation; 3) feeling out of control; 4) desire to do something to gain control; and 5) candid descriptions of AD/dementia. Both caregivers and care recipients reported physical, emotional, and psychological problems associated with AD/dementia. Additionally, both groups expressed involvement in research and advocacy and shared a variety of coping mechanisms with their readers. Of note, numerous care recipients expressed awareness of their cognitive decline, thus providing a unique perspective of individuals with AD/dementia. This study provided information about the experiences of AD/dementia caregivers and care recipients. Our findings revealed that both caregivers and care recipients can offer insight into the dementia experience. Health professionals could incorporate this information into future interventions to better support both AD/dementia caregivers and care recipients.

Assessing the impact of upper limb disability following stroke: a qualitative enquiry using internet-based personal accounts of stroke survivors

Purpose: Upper limb disability following stroke may have multiple effects on the individual. Existing assessment instruments tend to focus on impairment and function and may miss other changes that are personally important. This study aimed to identify personally significant impacts of upper limb disability following stroke. Methods: Accounts by stroke survivors, in the form of web-based diaries (blogs) and stories, were sought using a blog search engine and in stroke-related web-sites. Thematic analysis using the World Health Organisation’s International Classification of Functioning Disability and Health (ICF) was used to identify personal impacts of upper limb disability following stroke. Results: Ninety-nine sources from at least four countries were analysed. Many impacts were classifiable using the ICF, but a number of additional themes emerged, including emotional, cognitive and behavioural changes. Blogs and other web-based accounts were easily accessible and rich sources of data, although using them raised several methodological issues, including potential sample bias. Conclusions: A range of impacts was identified, some of which (such as use of information technology and alienation from the upper limb) are not addressed in current assessment instruments. They should be considered in post-stroke assessments. Blogs may help in the development of more comprehensive assessments.Implications for RehabilitationA comprehensive assessment of the upper limb following stroke should include the impact of upper limb problems on social participation, as well as associated emotional, cognitive and behavioural changes.Using personalised assessment instruments alongside standardised measures may help ensure that these broader domains are considered in discussions between clinicians and patients.Rehabilitation researchers should investigate whether and how these domains could be addressed and operationalised in standard upper limb assessment instruments.

Patients covertly recording clinical encounters: threat or opportunity? A qualitative analysis of online texts.

BackgroundThe phenomenon of patients covertly recording clinical encounters has generated controversial media reports. This study aims to examine the phenomenon and analyze the underlying issues.Methods and FindingsWe conducted a qualitative analysis of online posts, articles, blogs, and forums (texts) discussing patients covertly recording clinical encounters. Using Google and Google Blog search engines, we identified and analyzed 62 eligible texts published in multiple countries between 2006 and 2013. Thematic analysis revealed four key themes: 1) a new behavior that elicits strong reactions, both positive and negative, 2) an erosion of trust, 3) shifting patient-clinician roles and relationships, and 4) the existence of confused and conflicting responses. When patients covertly record clinical encounters – a behavior made possible by various digital recording technologies – strong reactions are evoked among a range of stakeholders. The behavior represents one consequence of an erosion of trust between patients and clinicians, and when discovered, leads to further deterioration of trust. Confused and conflicting responses to the phenomenon by patients and clinicians highlight the need for policy guidance.ConclusionsThis study describes strong reactions, both positive and negative, to the phenomenon of patients covertly recording clinical encounters. The availability of smartphones capable of digital recording, and shifting attitudes to patient-clinician relationships, seems to have led to this behavior, mostly viewed as a threat by clinicians but as a welcome and helpful innovation by some patients, possibly indicating a perception of subordination and a lack of empowerment. Further examination of this tension and its implications is needed.

Aphasia blog talk: How does stroke and aphasia affect a person’s social relationships?

Background: Stroke and aphasia can negatively affect a person’s ability to maintain healthy social relationships, both within the family and also with friends and the wider network. To date, this has been explored predominantly through qualitative interviews and questionnaires. Blogs written by people with aphasia constitute a novel source of data, comprised of people’s own voices on issues that are of concern to them.Aims: To explore the impact of stroke and aphasia on a person’s relationships with family, friends and the wider network through analysing blogs written by people with aphasia.Methods & Procedures: Blog search engines were used to identify blogs sustained by a sole author who had aphasia following a stroke, and which reflected on their social network. The data were analysed qualitatively using framework analysis.Outcomes & Results: The systematic search resulted in 10 relevant blogs. Participants were aged between 26 and 69 years old, lived in the community, were at least 1 year post stroke and included six women and four men. Aphasia was a consistent thread running through the blogs affecting conversations with all parts of a person’s network and impacting on participants’ sense of self. They found it more difficult to take part in family activities and described higher degrees of dependence and changed family dynamics. Contact with friends was reduced, partly due to communication and physical difficulties. While some participants became motivated to become members of groups post stroke, contact with the wider network sometimes diminished, in part because of loss of work and community activities. An additional factor impacting on social relationships was other people’s positive or negative reaction towards the person with aphasia. Finally, the blogs reflected on the importance of support they had received, both from close family and also from the wider community.Conclusions: This study found that social relationships played a crucial role in people’s lives following a stroke and aphasia. Nonetheless, family relationships, friendships and social exchanges within the wider social network were all substantially affected. Exploring this area through online narratives offered a rich and highly authentic source of data. The findings suggest that clinicians should incorporate social approaches in rehabilitation and consider ways to foster the maintenance of social networks. The use of social media by people with aphasia should be further explored, both as a therapeutic outlet and also as a way for people with aphasia to feel connected to a wider community.

Blosen: Blog Search Engine Based on Post Concept Clustering

This paper focuses on building a blog search engine which doesn't focus only on keyword search but includes extended search capabilities. It also incorporates the blog-post concept clustering which is based on the category extracted from the blog post semantic content analysis. The proposed approach is titled as "BloSen (Blog Search Engine)". It involves in extracting the posts from blogs and parsing them to extract the blog elements and store them as fields in a document format. Inverted index is being built on the fields of the documents. Search is induced on the index and requested query is processed based on the documents so far made from blog posts. It currently focuses on Blogger and Wordpress hosted blogs since both these hosting services are the most popular ones in the blogosphere. The proposed BloSen model is experimented with a prototype implementation and the results of the experiments with the user's relevance cumulative metric value of 95.44% confirms the efficiency of the proposed model.

UNIFIED SEMANTIC BLOG MINING FRAMEWORK AND SUMMARIZED BLOG RETRIEVAL

In today’s scenario, publishing the personal content and technical content has become very easy for the publishers, because of the readily available prominent social media called blog. Content developers, teachers, researchers post various articles relevant to their research or newly emerging topics. Blog content in one blog source may resemble the semantics of blog from other source. Readers, who are fascinated in reading the blog content, anticipate retrieving the blogs from different sources for their query. A large number of posts are available in the web. Hence the blog reader’s task becomes very complex to search the relevant content for their query. This study introduces a novel idea to collect the blog using unified Semantic Blog Mining Framework (SBMF). SBMF collects blogs from different blog sources using the ontology constructed for education domain. Blogs collected from different sources are collection which contains relevant or irrelevant blogs. The new blog summarizer summarizes the blog content and ranks the blogs according to the similarity of the blog with query given by the user. The proposed blog summarizer check the similarity of each sentence in a blog, sort the order of sentence based on the similarity of the text with query word and reduces the number of sentences. The experimental results shows that the proposed unified SBMF and blog summarizer produces better relevant and summarized number of blogs compared to various search engines. SBMF confirms the hundred percentage relevancy compared to other blog search engines. Blog summarizer yields accurate summarization for the collected blogs.

Research on Search Engine Marketing for Small and Medium-Sized Material Enterprises

This paper introduces the weakness of small and medium-sized material enterprises first, and then analyzes the primary component of search engine marketing according to the experience of the authors and other scholars. Combined with the practical situation of small and medium-sized material enterprises, this paper proposes to do search engine marketing from the angle of search engine optimization, paid listing, search engine blog marketing and search engine community marketing. Among them, search engine optimization as the major part of search engine marketing is introduced in detail and applied with specific instructions.

Exploring Library 2.0 on the Social Web

Library 2.0 literature has described many of the possibilities Web 2.0 technologies offer to libraries. Case studies have assessed local use, but no studies have measured the Library 2.0 phenomenon by searching public social networking sites. This study used library-specific terms to search public social networking sites, blog search engines, and social bookmarking sites for activity associated with librarians and library users. Blog search data about the recentness of activity or the popularity of a blog post indicate that Library 2.0 technology has many early adopters but provides less evidence of sustained use. The results follow a curve resembling the 80/20 rule and also resemble Chris Anderson's “long tail” effect, in which very few authors create the vast amount of content. These exploratory results can be used as a starting point for future studies. Librarians who use tags to describe Web-based content might use these findings to select more effective tags. Librarians implementing a blog or a social networking presence might use this study to balance the benefits with the amount of work required to maintain an up-to-date presence.

Using Blog Content Depth and Breadth to Access and Classify Blogs

Blogs are attractive to readers and researchers because of their ability to express a variety of opinions and critiques on topics. In particular, the fast growth of such online context brings in a strong demand for developing blog-specific filtering systems in order to identify and monitor the knowledge flow in the blogosphere. Traditional measures, which simply adopt link-analysis algorithms, are inadequate when it comes to blogs because of their sparseness of links. In this paper, we propose a filtering system that is able to assess the associated content of blog entries by measuring topic concentration and topic variety in terms of content depth and breadth with the scalars of informativeness, completeness, topic count, inter-topic distance, and topic mergence. These employed measures have proved to be appropriate for helping users to judge blogs they prefer among search results. Our system is different from existing blog search engines, as it aims to provide better relevance and precision of the search.

Word co-occurrences on Webpages as a measure of the relatedness of organizations: A new Webometrics concept

Web hyperlink analysis has been a key topic of Webometric research. However, inlink data collection from commercial search engines has been limited to only one source in recent years, which is not a promising prospect for the future development of the field. We need to tap into other Web data sources and to develop new methods. Toward this end, we propose a new Webometrics concept that is based on words rather than inlinks on Webpages. We propose that word co-occurrences on Webpages can be a measure of the relatedness of organizations. Word co-occurrence data can be collected from both general search engines and blog search engines, which expands data sources greatly. The proposed concept is tested in a group of companies in the LTE and WiMax sectors of the telecommunications industry. Data on the co-occurrences of company names on Webpages were collected from Google and Google Blog. The co-occurrence matrices were analyzed using MDS. The resulting MDS maps were compared with industry reality and with the MDS maps from co-link analysis. Results show that Web co-word analysis could potentially be as useful as Web co-link analysis. Google Blog seems to be a better source than Google for co-word data collection.

Detecting spam blogs from blog search results

Blogging has been an emerging media for people to express themselves. However, the presence of spam blogs (also known as splogs) may reduce the value of blogs and blog search engines. Hence, splog detection has recently attracted much attention from research. Most existing works on splog detection identify splogs using their content/link features and target on spam filters protecting blog search engines’ index from spam. In this paper, we propose a splog detection framework by monitoring the on-line search results. The novelty of our splog detection is that our detection capitalizes on the results returned by search engines. The proposed method therefore is particularly useful in detecting those splogs that have successfully slipped through the spam filters that are also actively generating spam-posts. More specifically, our method monitors the top-ranked results of a sequence of temporally-ordered queries and detects splogs based on blogs’ temporal behavior. The temporal behavior of a blog is maintained in a blog profile. Given blog profiles, splog detecting functions have been proposed and evaluated using real data collected from a popular blog search engine. Our experiments have demonstrated that splogs could be detected with high accuracy. The proposed method can be implemented on top of any existing blog search engine without intrusion to the latter.

CUSTOMER SEGMENTATION AND CLASSIFICATION FROM BLOGS BY USING DATA MINING: AN EXAMPLE OF VOIP PHONE

Blogs have been considered the 4th Internet application that can cause radical changes in the world, after e-mail, instant messaging, and Bulletin Board System (BBS). Many Internet users rely heavily on them to express their emotions and personal comments on whatever topics interest them. Nowadays, blogs have become the popular media and could be viewed as new marketing channels. Depending on the blog search engine, Technorati, we tracked about 94 million blogs in August 2007. It also reported that a whole new blog is created every 7.4 seconds and 275,000 blogs are updated daily. These figures can be used to illustrate the reason why more and more companies attempt to discover useful knowledge from this vast number of blogs for business purposes. Therefore, blog mining could be a new trend of web mining. The major objective of this study is to present a structure that includes unsupervised (self-organizing map) and supervised learning methods (back-propagation neural networks, decision tree, and support vector machines) for extracting knowledge from blogs, namely, a blog mining (BM) model. Moreover, a real case regarding VoIP (Voice over Internet Protocol) phone products is provided to demonstrate the effectiveness of the proposed method.

Exploring the Relationship between Keywords and Feed Elements in Blog Post Search

Blogs are increasingly accepted as a useful means to proliferate a variety of information on the web. As the popularity of blogs grows rapidly, a number of blog search engines have appeared recently to help users access and discover blog posts efficiently. Nevertheless, existing approaches tend to focus on ranking the blog posts according to their recency or popularity only, leaving the problem of retrieving more topic relevant posts to a user's query largely unexplored. In this paper, we present a novel blog ranking framework, called PTRank, that improves search quality by taking account of relevance feedback from users as well as various information available from RSS feeds. A neural network method is employed to learn ranking functions that provide a relevance score between a keyword and a blog post. Extensive experiments on real blog data have been conducted to validate the proposed ranking framework for blog post search, and the results indicate that PTRank performs significantly better than the existing popular approach.

A blog article recommendation generating mechanism using an SBACPSO algorithm

In recent years blog-assisted learning has been used widely in higher education for improving writing and collaboratively sharing work online. However, methods for gathering useful information to be used as auxiliary-learning materials from the multitude of blog articles in the blogosphere has been seldom investigated. This paper proposes an individualized blog article recommendation mechanism to provide quality blog articles that accord with users’ learning topics. First, an IR-based technique was applied to extract and score index terms. The top three index terms were then entered into Google’s blog search engine to find the raw recommended blog articles. To avoid the situation where frequent topic-changing leads to a deficiency of article data on a specific learning topic, a forgetting rate was employed to simulate the phenomenon of changing learning topics. Subsequently, an extended Serial Blog Article Composition Particle Swarm Optimization (SBACPSO) algorithm was employed to provide optimal recommended materials to users. We evaluated the system’s performance to find the appropriate article population size. Finally, user satisfaction regarding both the system and recommended content were gauged to find the system’s limitations and possible improvements. This study is of importance in that it provides users with dynamic blog article recommendation, improved online information discovery skills and opportunities to socialize with other bloggers.

Blogs – The New Source of Data Analysis

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Blog search engines

PurposeThe purpose of this article is to explore the capabilities and limitations of weblog search engines.Design/methodology/approachThe features of a range of current blog search engines are described. These are then discussed and illustrated with examples that illustrate the reliability and coverage limitations of blog searching.FindingsAlthough blog searching is a useful new technique, the results are sensitive to the choice of search engine, the parameters used and the date of the search. The quantity of spam also varies by search engine and search type.Research limitations/implicationsThe results illustrate blog search evaluation methods and do not use a full‐scale scientific experiment.Originality/valueBlog searching is a new technique, and one that is significantly different from web searching. Hence information professionals need to understand its strengths and weaknesses.