Microposts Ontology Construction Via Concept Extraction

Information and resources available on the Web are growing increasingly and web users need to have a common understanding of them. The Semantic Web whose most important role is to help machine to understand and analyze the existing data on the Web, has not been used commonly, yet. The foundation of the Semantic Web are ontologies. Ontologies play the main role in the exchange of information and development of the Lexical Web to the Semantic Web. Manual construction of ontologies is time-consuming, expensive, and dependent on the knowledge of domain engineers. Also, Ontologies that have been extracted automatically from corpus on the Web might have incomplete information. The main objective of this study is describing a method to improve and expand the information of the ontologies. Therefore, this study first discusses the automatic construction of prototype ontology in animals‟ domain from Wikipedia and then a method is presented to improve the built ontology. The proposed method of improving ontology expands ontology concepts through Bootstrapping methods using a set of concepts and relations in initial ontology and with the help of the Google search engine. A confidence measure was considered to choose the best option from the returned results by Google. Finally, the experiments showed the information that was obtained using the proposed method is twice more accurate than the information that was obtained at the stage of automatic construction of ontology from Wikipedia.

IntroductionDrug utilization is currently assessed through traditional data sources such as big electronic medical records (EMRs) databases, surveys, and medication sales. Social media and internet data have been reported to provide more accessible and more timely access to medications' utilization.ObjectiveThis review aims at providing evidence comparing web data on drug utilization to other sources before the COVID-19 pandemic.MethodsWe searched Medline, EMBASE, Web of Science, and Scopus until November 25th, 2019, using a predefined search strategy. Two independent reviewers conducted screening and data extraction.ResultsOf 6,563 (64%) deduplicated publications retrieved, 14 (0.2%) were included. All studies showed positive associations between drug utilization information from web and comparison data using very different methods. A total of nine (64%) studies found positive linear correlations in drug utilization between web and comparison data. Five studies reported association using other methods: One study reported similar drug popularity rankings using both data sources. Two studies developed prediction models for future drug consumption, including both web and comparison data, and two studies conducted ecological analyses but did not quantitatively compare data sources. According to the STROBE, RECORD, and RECORD-PE checklists, overall reporting quality was mediocre. Many items were left blank as they were out of scope for the type of study investigated.ConclusionOur results demonstrate the potential of web data for assessing drug utilization, although the field is still in a nascent period of investigation. Ultimately, social media and internet search data could be used to get a quick preliminary quantification of drug use in real time. Additional studies on the topic should use more standardized methodologies on different sets of drugs in order to confirm these findings. In addition, currently available checklists for study quality of reporting would need to be adapted to these new sources of scientific information.

Social media is the collection of different social networks containing different type of information. The information may be in the form of text, video, audio and image. Also various categories of users, various types of communities are available on social network. This research reports on the extraction of new keywords from messages posted on social media which will be helpful in the identification of various communities, category of user and hidden pattern present in the social media. In this paper, we applied Probalistic approach to recognize the new keywords and assign the group accordingly. State-of-the- art studies performed detection on the basis of existing keywords but the proposed approach take decision based on the existing keywords and also on new keywords extracted from social media.

Navigating Social Media in #Ophthalmology

Enhancing relevant concepts extraction for ontology learning using domain time relevance

Solace in Social Media: Women Unite Under COVID-19

Huge amount of user generated contents are being created in major social media everyday. It’s very difficult for users to quickly grasp the most important topics from such big data. Document clustering techniques are often used for topic detection from news, but it’s still challenging for social media. Firstly, since social media posts are usually very short, it’s hard to capture their semantic meanings. Secondly, given huge amount of social media posts, clustering effectiveness becomes unacceptable. Among agglomerative hierarchical clustering methods, centroid clustering is much more efficient, but with the issue of inversion or reversals. In this paper, we propose to detect popular topics from social media posts using dot-product similarity based centroid clustering of their word embeddings. Firstly, we extract keywords in posts with word segmentation, where documents are represented by word embedding of keywords. Secondly, various topics are extracted from the clustering results of social media posts by calculating dot-product similarity of their word embeddings. Finally, the popularity of each topic is estimated by the aggregate sentiment ratings from user replies. From the experimental results on PTT discussion forum, centroid clustering with dot-product similarity of word embeddings achieves better clustering efficiency with comparable effectiveness in terms of Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI). Further investigation is needed to verify the effectiveness in different social media sources.

Tourism is growing very rapidly with the development of technology, especially information technology .Through information technology people can easily get access to information about various destinations and tourist attractions as well as hotel by online media communication. Therefore, this study was trying to use social media such as facebook and twitter, where both social media were widely used by companies engaged in tourism in the world as well as in Bali. Internet consists of several websites, but the most effective one to conduct the research on tourism such as webseries 2.0 or commonly called as social networking media. Social medias - Facebook and Twitter - both were wildly used by the companies running in the sector of tourism in Bali, such as Hotels, Villas, Restaurants, Spas, Travel Agents, Airlines, and Tourist Attraction Managements, to advertised their products and services in their respective field. Through those social media, tourists will be able to access the comments or reviews about the companies. The problem raised in this research is how Bali’s tourism data in social networking media were used and considered, and how was the image of Bali’s tourism in their point of viewbased on the research. The method used here was descriptive qualitative method which describes this phenomenon descriptively by analyzing the obtained comments on social networking media such as facebook and twitter, and then classified in the form of comments positive, negative, and unidentified comment. Afterwards comments were re-analyzed through 4A Approach which consist of Attraction, amenities, Ancillary, and Accessibility. Data which has been obtained re-analyzed via the data obtained from the homepage of (TripAdvisor and Agoda.com). In addition, to strengthen the research data obtained from the homepage of TripAdvisor and Agoda.com , then it has been elaborated on the point rating system which indicatedthat the company were registered in both of those social networking media such as Hotel, Villa, SPA, restaurants, travel agents, airlines, media, tourist attraction management has a good rating result, and it has represented that the image of Bali tourism has a good rank in the eyes of local and international tourists. This data was also strengthened by the existence of empirical studies where there were the data from a hundred tourists came to Bali both locally and internationally has filled out the questionnaire about Bali in order to obtain maximum results on the image of Bali tourism. The results showed that, social networking media were effectively used in this study. Many companies in Bali used facebook and twitter. In addition, Bali Tourism image placed in positive rank on both of social networking sites, and it was gained by the results of questionnaires spread towards the visitor.

With the advent of mobile and internet technology, social media has experienced a tremendous growth over the past few years. Social networking site such as Twitter provides the platform for content generation, information dissemination and communications. Summarizing the textual contents or extracting the influential words is a challenging task. Therefore an unsupervised graph based approach is proposed for automatic Keyword Extraction from the collection of Tweets using Node and Edge Weight (KETNEW). The node weight and edge weight depends on various parameters such as tweet frequency, position of node, clustering coefficient, co-occurrence frequency and shared neighbors. The important aspect of our approach is that it is a hybrid approach i.e. a graph based approach (structural approach) which depends upon statistical as well as linguistic features. Apart from this, it also depends on the position of the word in the text. These numerous features made it perform better than existing techniques.

With the advent of social network services such as Twitter, Facebook, Tumbler, and Google+, the research on social network and media analysis has been greatly advanced. In recent years, the interactions among people, sharing of knowledge and experiences, community activities in social network services increase greatly, which would make the research on social networks more important. Furthermore, as social media contents within social network services are rapidly being produced and consumed, the social media contents now account for the majority of content published on the world wide web. Social media is differentiated from traditional media in many aspects such as its frequency, quality, usability, immediacy, and permanence, which leads to significant potential to the social media analysis research.

The aim of this study is to reveal Open Education related social media usage in Turkey through social network analyses. To this end, the most widely used social media network in Turkey, Facebook, was chosen. All the pages and groups created on Facebook related to Open Education were found. A total of 207 groups and 521 pages were accessed and entered into the social network analysis. Within the scope of this study, the density and centrality features of the network were analyzed for groups whereas the density, centrality, and degrees features of the network were analyzed for pages. The study's results revealed that while pages and groups related to programs emphasizing current job opportunities and student support services were more actively used, commercial accounts attempted to exploit students' exam anxiety instead of generating actual content. The findings also revealed that well structured institutional pages appeal to students and have the potential to reach high levels of popular in short periods of time.

The Hypertext 2015 Doctorial Consortium session is held the first day of the 26th ACM Conference on Hypertext and Social Media. It offers Ph.D. students an opportunity to present their ongoing research towards obtaining a Ph.D. degree in the disciplines related to the conference, mostly in Computer and Information Sciences but not limited to them. The themes of the submissions are strongly connected to the research tracks of this year's conference: Digital Connectivity, Data Connectivity and Digital Humanities. The Digital Connectivity track targets developing insights into the mechanisms of information generation and dissemination, characterization of evolutionary processes on online social networks, and studies of models and systems that support these processes. The second track, Data Connectivity, deals with the methods, techniques and technologies that can be used to make data available on the Web, with a special focus on how heterogeneous data sources can be connected to each other. Finally, the track of Digital Humanities seeks to attract work from an interdisciplinary perspective, on the intersection between computer science on one hand, and the humanities and social sciences on the other. During the DC session, students receive constructive feedback from their peers and from a panel of mentors to support them in envisioning added-value contributions to the state-of-the-art research in Hypertext and Social Media. The consortium session is open to all doctorial students by application. Each submission was reviewed by three senior researchers in the topics the students made their submissions. The DC session is aimed in particular at students who have defined a dissertation topic but are still more than one year from graduating at the time of application in order to obtain benefits from feedback. This year, the committee has accepted three contributions to be presented during the Doctorial Consortium session: Automated Methods for Identity Resolution in Heterogeneous Social Platforms by Paridhi Jahin. In this work, connected to the digital and data connectivity tracks of the conference, the author proposes novel methods to search and link user identities scattered across heterogeneous social networks. The methods consider carefully users' privacy, so they are designed to access only public and historic data. The evaluation is proposed on large datasets over multiple platforms to prove their significance in identity resolution of an online user. Language Innovation and Change in On-line Social Networks by Daniel Kershaw. As a fundamental aspect for human communication, this research focuses on forecasting online language change through the use of predictive and descriptive methodologies. This work is framed within structuration theory which helps the researcher in structuring the analysis of the dynamics of language (re)production - i.e. by the agent (user), the social structure and their interplay. A Framework to Provide Customized Reuse of Open Corpus Content for Adaptive Systems by Mostafa Bayomi. This work deals with issues of reusability of contents on adaptive systems. Adaptive systems tailor content specific to user's needs, but they face two big problems: (a) they use a closed corpus content that has been prepared for them a priori, and (b) the content is tightly coupled with other parts of the system, which hinders its reusability. This work presents a proposal that leverages the semantic web by extending an existing content provision system, Slice-pedia.

The Semantic Web uses formal ontologies as a key instrument in order to add structure to the data, but building domain specific ontologies is still a difficult, time consuming and error-prone process since most information is currently available as free-text. Therefore the development of fast and cheap solutions for ontology learning from text is a key factor for the success and large scale adoption of the Semantic Web. Ontology development is primarily concerned with the definition of concepts and relations between them, so one of the fundamental research problems related to ontology learning is the extraction of concepts from text. To investigate this research problem we focus on the expert finding problem, i.e, the extraction of expertise topics and their assignment to individuals. The ontological concepts we extract are a person's skills, knowledge, behaviours, and capabilities. For increased efficiency, competitiveness and innovation, every company has to facilitate the identification of experts among its workforce. Even though this can be achieved by using the information gathered during the employment process and through self-assessment, a person's competencies are likely to change over time. Information about people's expertise is contained in documents available inside an organisation such as technical reports but also in publicly available resources, e.g., research articles, wiki pages, blogs, other user-generated content. The human effort required for competency management can be reduced by automatically identifying the experts and expertise topics from text. Our goal is to explore how existing technologies for concept extraction can be advanced and specialised for extracting expertise topics from text in order to build expertise profiles.

Construction of fuzzy ontologies from fuzzy XML models

Ontology is playing an increasingly important role in knowledge management and the Semantic Web. The tourism information ontology is becoming a core research field in the realm of information retrieval. The objective of this study is to explore the potential role of Formal Concept Analysis (FCA) in a web-based ontology construction support system in a tourism domain. An ontology construction method known as TOCM (Tourism Ontology Construction Method) that can generate an ontology based on Formal Concept Analysis theory is proposed. Under the framework of our ontology construction method, the knowledge engineers could reach a new ontology of tourism information that was demonstrated as useful to support their ontology construction tasks.