111,975 research outputs found

    SOCIAL MEDIA IN MODERN BUSINESS

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    Social media help companies to reach new customers. New areas where companies can use social media include web-based training, team-based projects, distribution of updates about plans and activities to employees, search for new offers and verification of information during staff recruitment. The purpose of this article is to identify possible trends in the use of social media for enhancing the performance of modern business ventures. This paper compares selected classifications of the Internet development phases. The rule of content cocreation and sharing, typical of Web 2.0, remains valid during the subsequent stage of development, i.e. Web 3.0. A qualitative difference consists in adding a new function of using semantic analysis of messages posted in the virtual space, most notably in the social media. Semantic analysis is applied primarily in order to adjust the products offered to consumers’ needs. Application of semantic tools may also be associated with information exclusion. This paper also analyzes the implications of semantic web in the new context, the effect of information extraction from the social media

    Folks in Folksonomies: Social Link Prediction from Shared Metadata

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    Web 2.0 applications have attracted a considerable amount of attention because their open-ended nature allows users to create light-weight semantic scaffolding to organize and share content. To date, the interplay of the social and semantic components of social media has been only partially explored. Here we focus on Flickr and Last.fm, two social media systems in which we can relate the tagging activity of the users with an explicit representation of their social network. We show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local alignment between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar topical interests are more likely to be friends, and therefore semantic similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on the Last.fm data set, confirming that the social network constructed from semantic similarity captures actual friendship more accurately than Last.fm's suggestions based on listening patterns.Comment: http://portal.acm.org/citation.cfm?doid=1718487.171852

    Event Organization 101: Understanding Latent Factors of Event Popularity

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    The problem of understanding people's participation in real-world events has been a subject of active research and can offer valuable insights for human behavior analysis and event-related recommendation/advertisement. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in three major cities around the world. We have conducted modeling analysis of four contextual factors (spatial, group, temporal, and semantic), and also developed a group-based social influence propagation network to model group-specific influences on events. By combining the Contextual features And Social Influence NetwOrk, our integrated prediction framework CASINO can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Evaluations demonstrate that our CASINO framework achieves high prediction accuracy with contributions from all the latent features we capture.Comment: International AAAI Conference on Web and Social Media (ICWSM) 2017 https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/1557

    Political communication of Hungarian parties in social networking platforms

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    In recent years, social media platforms are said to have a major impact on communication and communication technologies. More specifically, popular social networking platforms are increasingly employed in political context. Thus, this study examines the online performance of activities and approaches for political communication between Hungarian political parties and civilians in social networking platforms, video hosting services, as well as microblogging services. In order to examine these connections, the author conducted a web-based quantitate analysis and a semantic sentiment analysis to calculate the efficiency and sentiment of social media posts created by political parties. According to the research results, Hungarian political parties underutilize the inherent communication potential of social networking platforms, especially on YouTube and Twitter

    SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles

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    SocialLink is a project designed to match social media profiles on Twitter to corresponding entities in DBpedia. Built to bridge the vibrant Twitter social media world and the Linked Open Data cloud, SocialLink enables knowledge transfer between the two, both assisting Semantic Web practitioners in better harvesting the vast amounts of information available on Twitter and allowing leveraging of DBpedia data for social media analysis tasks. In this paper, we further extend the original SocialLink approach by exploiting graph-based features based on both DBpedia and Twitter, represented as graph embeddings learned from vast amounts of unlabeled data. The introduction of such new features required to redesign our deep neural network-based candidate selection algorithm and, as a result, we experimentally demonstrate a significant improvement of the performances of SocialLink

    Two Sides of the Same Coin? Analysis of the Web-Based Social Media with Regard to the Image of the Agri-Food Sector in Germany

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     Never before has food been as safe and secure as it is today, but simultaneously, society has become increasingly critical towards agricultural and food related issues. This two-sided development between society and agribusiness will be analyzed using Framing Theory. A quantitative semantic analysis was applied to evaluate the web-based social media in Germany. 50,931 web posts were collected covering 21 issues identified as relevant for the agri-food sector. The results show that all contentious issues are mainly framed in a two-sided way. The modern productivity-driven industry is judged as a negative development, trends returning to a more natural food production are seen as positive

    Towards the Development of a Framework for Socially Responsible Software by Analyzing Social Media Big Data on Cloud Through Ontological Engineering

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    AbstractA socially responsible internet is the need of the hour considering its huge potential and role in educating and transforming the society. Social computing is emerging as an important area as far as development of next generation web is concerned. With the proliferation of social networking applications, vast amount of data is available on cloud, which may be analyzed to gain useful insight into behavioral and linguistic patterns of different cultural and socio-economic groups further classified on the basis of gender and age etc. The idea is to come up with an appropriate framework for socially responsible software artifacts. These artifacts will monitor online social network data and analyze it from the perspective of socially responsible behavior based on ontological engineering concepts. Identification of socially responsible agents is such an example, though based on a different approach. More examples may be taken from literature dealing with microblog analytics, social semantic web, upper ontology for social web, and social-network-sourced big data analytics. In the present work, it is proposed to focus on analysis/monitoring of socially responsible behavior of social media big data and develop an upper level ontology as the framework/tool for such an analytics

    Enabling Scalable Multi-channel Communication through Semantic Technologies

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    With the advance of the Web in the direction Social Media the number of communication possibilities has exponentially increased bringing new challenges and opportunities for companies to build and shape their reputation online as well as to engage and maintain the relationships to their customers. In this paper we describe how semantic technologies enable scalable, effective and efficient on-line communication. We illustrate four different ways in which semantics can be used for this purpose. First, we discuss semantic analysis of communication items based on 'classical' semantic, such as natural language processing. Second, we look at semantics as a channel, viewing Linked Open Data vocabularies not only as terminological assets but as communication channels. Third, semantics provide the methodologies and tools for content modeling by means of ontologies. Finally, semantics through semantic matchmaking enable semi-automatic assignment and distribution of content to channels and vice-versa

    Usefulness of social tagging in organizing and providing access to the web: An analysis of indexing consistency and quality

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    This dissertation research points out major challenging problems with current Knowledge Organization (KO) systems, such as subject gateways or web directories: (1) the current systems use traditional knowledge organization systems based on controlled vocabulary which is not very well suited to web resources, and (2) information is organized by professionals not by users, which means it does not reflect intuitively and instantaneously expressed users’ current needs. In order to explore users’ needs, I examined social tags which are user-generated uncontrolled vocabulary. As investment in professionally-developed subject gateways and web directories diminishes (support for both BUBL and Intute, examined in this study, is being discontinued), understanding characteristics of social tagging becomes even more critical. Several researchers have discussed social tagging behavior and its usefulness for classification or retrieval; however, further research is needed to qualitatively and quantitatively investigate social tagging in order to verify its quality and benefit. This research particularly examined the indexing consistency of social tagging in comparison to professional indexing to examine the quality and efficacy of tagging. The data analysis was divided into three phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of tag attributes. Most indexing consistency studies have been conducted with a small number of professional indexers, and they tended to exclude users. Furthermore, the studies mainly have focused on physical library collections. This dissertation research bridged these gaps by (1) extending the scope of resources to various web documents indexed by users and (2) employing the Information Retrieval (IR) Vector Space Model (VSM) - based indexing consistency method since it is suitable for dealing with a large number of indexers. As a second phase, an analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. Finally, to investigate tagging pattern and behaviors, a content analysis on tag attributes was conducted based on the FRBR model. The findings revealed that there was greater consistency over all subjects among taggers compared to that for two groups of professionals. The analysis of tagging exhaustivity and tag specificity in relation to tagging effectiveness was conducted to ameliorate difficulties associated with limitations in the analysis of indexing consistency based on only the quantitative measures of vocabulary matching. Examination of exhaustivity and specificity of social tags provided insights into particular characteristics of tagging behavior and its variation across subjects. To further investigate the quality of tags, a Latent Semantic Analysis (LSA) was conducted to determine to what extent tags are conceptually related to professionals’ keywords and it was found that tags of higher specificity tended to have a higher semantic relatedness to professionals’ keywords. This leads to the conclusion that the term’s power as a differentiator is related to its semantic relatedness to documents. The findings on tag attributes identified the important bibliographic attributes of tags beyond describing subjects or topics of a document. The findings also showed that tags have essential attributes matching those defined in FRBR. Furthermore, in terms of specific subject areas, the findings originally identified that taggers exhibited different tagging behaviors representing distinctive features and tendencies on web documents characterizing digital heterogeneous media resources. These results have led to the conclusion that there should be an increased awareness of diverse user needs by subject in order to improve metadata in practical applications. This dissertation research is the first necessary step to utilize social tagging in digital information organization by verifying the quality and efficacy of social tagging. This dissertation research combined both quantitative (statistics) and qualitative (content analysis using FRBR) approaches to vocabulary analysis of tags which provided a more complete examination of the quality of tags. Through the detailed analysis of tag properties undertaken in this dissertation, we have a clearer understanding of the extent to which social tagging can be used to replace (and in some cases to improve upon) professional indexing
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