Search (38 results, page 1 of 2)

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Abstract

The present challenge faced by scientists working with Big Data comes in the overwhelming volume and level of detail provided by current data sets. Exceeding traditional empirical approaches, Big Data opens a new perspective on scientific work in which data comes to play a role in the development of the scientific problematic to be developed. Addressing this reconfiguration of our relationship with data through readings of Wittgenstein, Macherey, and Popper, we propose a picture of science that encourages scientists to engage with the data in a direct way, using the data itself as an instrument for scientific investigation. Using GIS as a theme, we develop the concept of cyber-human systems of thought and understanding to bridge the divide between representative (theoretical) thinking and (non-theoretical) data-driven science. At the foundation of these systems, we invoke the concept of the "semantic pixel" to establish a logical and virtual space linking data and the work of scientists. It is with this discussion of the relationship between analysts in their pursuit of knowledge and the rise of Big Data that this present discussion of the philosophical foundations of Big Data addresses the central questions raised by social informatics research.

Date

7. 3.2019 16:32:22

Footnote

Beitrag eines Special issue on social informatics of knowledge

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Abstract

With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.

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Abstract

Despite the rapid growth in social network sites and in data mining for emotion (sentiment analysis), little research has tied the two together, and none has had social science goals. This article examines the extent to which emotion is present in MySpace comments, using a combination of data mining and content analysis, and exploring age and gender. A random sample of 819 public comments to or from U.S. users was manually classified for strength of positive and negative emotion. Two thirds of the comments expressed positive emotion, but a minority (20%) contained negative emotion, confirming that MySpace is an extraordinarily emotion-rich environment. Females are likely to give and receive more positive comments than are males, but there is no difference for negative comments. It is thus possible that females are more successful social network site users partly because of their greater ability to textually harness positive affect.

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Abstract

A challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academic and industry. To address this challenge, semantic analysis of textual data is focused in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyse the social data at two levels i.e. the entity level and the domain level. We have chosen Twitter as a social channel challenge for a purpose of concept proof. Domain knowledge is captured in ontologies which are then used to enrich the semantics of tweets provided with specific semantic conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification.

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Abstract

Geolocated social media data provide a powerful source of information about places and regional human behavior. Because only a small amount of social media data have been geolocation-annotated, inference techniques play a substantial role to increase the volume of annotated data. Conventional research in this area has been based on the text content of posts from a given user or the social network of the user, with some recent crossovers between the text- and network-based approaches. This paper proposes a novel approach to categorize highly-mentioned users (celebrities) into Local and Global types, and consequently use Local celebrities as location indicators. A label propagation algorithm is then used over the refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments over three standard Twitter benchmark data sets demonstrate that our approach outperforms state-of-the-art user geolocation methods.

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Abstract

Social network sites (SNSs) such as MySpace and Facebook are important venues for interpersonal communication, especially among youth. One way in which members can communicate is to write public messages on each other's profile, but how is this unusual means of communication used in practice? An analysis of 2,293 public comment exchanges extracted from large samples of U.S. and U.K. MySpace members found them to be relatively rapid, but rarely used for prolonged exchanges. They seem to fulfill two purposes: making initial contact and keeping in touch occasionally such as at birthdays and other important dates. Although about half of the dialogs seem to exchange some gossip, the dialogs seem typically too short to play the role of gossip-based social grooming for typical pairs of Friends, but close Friends may still communicate extensively in SNSs with other methods.

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Abstract

Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the 'master miners' allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.

Source

Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini

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Abstract

Examines the relationship between systems and users from the knowledge discovery in databases or data mining perspecitives. A comprehensive study on knowledge discovery in human computer symbiosis is needed. Proposes a database-user adversarial partnership, which is general enough to cover knowledge discovery and security of issues related to databases and their users. It can be further generalized into system-user adversarial paertnership. Discusses opportunities provided by knowledge discovery techniques and potential social implications

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Abstract

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

Content

Inhalt: An Introduction to Text Mining.- Information Extraction from Text.- A Survey of Text Summarization Techniques.- A Survey of Text Clustering Algorithms.- Dimensionality Reduction and Topic Modeling.- A Survey of Text Classification Algorithms.- Transfer Learning for Text Mining.- Probabilistic Models for Text Mining.- Mining Text Streams.- Translingual Mining from Text Data.- Text Mining in Multimedia.- Text Analytics in Social Media.- A Survey of Opinion Mining and Sentiment Analysis.- Biomedical Text Mining: A Survey of Recent Progress.- Index.

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Abstract

A 5-year project to study scientific data uses in geography, starting in 1999, evolved into 20 years of research on data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the "team science" approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards's model of "making global data"-collecting signals via consistent methods, technologies, and policies-to "make data global"-comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations of students and staff, transitions between grants, transfer of legacy data between software tools, research methods, and the role of professional data managers in the social sciences.

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Abstract

Topic discovery is an important means for marketing, e-Business and social science studies. As well, it can be applied to various purposes, such as identifying a group with certain properties and observing the emergence and diminishment of a certain cyber community. Previous topic discovery work (J.M. Kleinberg, Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, California, p. 668) requires manual judgment of usefulness of outcomes and is thus incapable of handling the explosive growth of the Internet. In this paper, we propose the Automatic Topic Discovery (ATD) method, which combines a method of base set construction, a clustering algorithm and an iterative principal eigenvector computation method to discover the topics relevant to a given query without using manual examination. Given a query, ATD returns with topics associated with the query and top representative pages for each topic. Our experiments show that the ATD method performs better than the traditional eigenvector method in terms of computation time and topic discovery quality.

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Abstract

We provide a brief, non-technical introduction to the text mining methodology known as "topic modeling." We summarize the theory and background of the method and discuss what kinds of things are found by topic models. Using a text corpus comprised of the eight articles from the special issue of Poetics on the subject of topic models, we run a topic model on these articles, both as a way to introduce the methodology and also to help summarize some of the ways in which social and cultural scientists are using topic models. We review some of the critiques and debates over the use of the method and finally, we link these developments back to some of the original innovations in the field of content analysis that were pioneered by Harold D. Lasswell and colleagues during and just after World War II.

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Abstract

We propose a text classification tool based on support vector machines for the assessment of organizational leadership styles, as appearing to Twitter users. We collected Twitter data over 51 days, related to the first 30 Italian organizations in the 2015 ranking of Forbes Global 2000-out of which we selected the five with the most relevant volumes of tweets. We analyzed the communication of the company leaders, together with the dialogue among the stakeholders of each company, to understand the association with perceived leadership styles and dimensions. To assess leadership profiles, we referred to the 10-factor model developed by Barchiesi and La Bella in 2007. We maintain the distinctiveness of the approach we propose, as it allows a rapid assessment of the perceived leadership capabilities of an enterprise, as they emerge from its social media interactions. It can also be used to show how companies respond and manage their communication when specific events take place, and to assess their stakeholder's reactions.

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Abstract

The emergence of large multi-institutional digital libraries has opened the door to aggregate-level examinations of the published word. Such large-scale analysis offers a new way to pursue traditional problems in the humanities and social sciences, using digital methods to ask routine questions of large corpora. However, inquiry into multiple centuries of books is constrained by the burdens of scale, where statistical inference is technically complex and limited by hurdles to access and flexibility. This work examines the role that exploratory data analysis and visualization tools may play in understanding large bibliographic datasets. We present one such tool, HathiTrust+Bookworm, which allows multifaceted exploration of the multimillion work HathiTrust Digital Library, and center it in the broader space of scholarly tools for exploratory data analysis.

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Date

2. 4.2000 18:01:22

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Abstract

Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.

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Abstract

Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.

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Abstract

What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining.

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Abstract

Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of "learning analytics," this work can-and often does-surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.

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Footnote

A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997