We present VOSviewer, a freely available computer program that we have developed for constructing and viewing bibliometric maps. Unlike most computer programs that are used for bibliometric mapping, VOSviewer pays special attention to the graphical representation of bibliometric maps. The functionality of VOSviewer is especially useful for displaying large bibliometric maps in an easy-to-interpret way. The paper consists of three parts. In the first part, an overview of VOSviewer’s functionality for displaying bibliometric maps is provided. In the second part, the technical implementation of specific parts of the program is discussed. Finally, in the third part, VOSviewer’s ability to handle large maps is demonstrated by using the program to construct and display a co-citation map of 5,000 major scientific journals.

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Software survey: VOSviewer, a computer program for bibliometric mapping.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

http://cfile28.uf.tistory.com/attach/23159E3D57117DF7107077

Twitter mood predicts the stock market.

We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

The spread of true and false news online

Scholarly usage data provides unique opportunities to address the known shortcomings of citation analysis. However, the collection, processing and analysis of usage data remains an area of active research. This article provides a review of the state-of-the-art in usage-based informetric, i.e. the use of usage data to study the scholarly process.

Usage Bibliometrics

Most individuals in social networks experience a so-called Friendship Paradox: they are less popular than their friends on average. This effect may explain recent findings that widespread social network media use leads to reduced happiness. However the relation between popularity and happiness is poorly understood. A Friendship paradox does not necessarily imply a Happiness paradox where most individuals are less happy than their friends. Here we report the first direct observation of a significant Happiness Paradox in a large-scale online social network of 39,110 Twitter users. Our results reveal that popular individuals are indeed happier and that a majority of individuals experience a significant Happiness paradox. The magnitude of the latter effect is shaped by complex interactions between individual popularity, happiness, and the fact that users strongly cluster by similar level of happiness. Our results indicate that the topology of online social networks, combined with how happiness is distributed in some populations, may be associated with significant psycho-social effects.

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The happiness paradox: your friends are happier than you

There exist ample demonstrations that indicators of scholarly impact analogous to the citation-based ISI Impact Factor can be derived from usage data; however, so far, usage can practically be recorded only at the level of distinct information services. This leads to community-specific assessments of scholarly impact that are difficult to generalize to the global scholarly community. In contrast, the ISI Impact Factor is based on citation data and thereby represents the global community of scholarly authors. The objective of this study is to examine the effects of community characteristics on assessments of scholarly impact from usage. We define a journal Usage Impact Factor that mimics the definition of the Thomson Scientific ISI Impact Factor. Usage Impact Factor rankings are calculated on the basis of a large-scale usage dataset recorded by the linking servers of the California State University system from 2003 to 2005. The resulting journal rankings are then compared to the Thomson Scientific ISI Impact Factor that is used as a reference indicator of general impact. Our results indicate that the particular scientific and demographic characteristics of a discipline have a strong effect on resulting usage-based assessments of scholarly impact. In particular, we observed that as the number of graduate students and faculty increases in a particular discipline, Usage Impact Factor rankings will converge more strongly with the ISI Impact Factor. © 2008 Wiley Periodicals, Inc.

Usage impact factor: The effects of sample characteristics on usage-based impact metrics

At present, digital library (DL) policy is largely informed by management intuition and coarse measures of user satisfaction. Most DLs, however, maintain extensive server logs of user retrieval requests that contain a wealth of information on user preferences and the structure of user retrieval patterns. We propose a quantitative approach to DL evaluation that analyzes the retrieval habits of users to assess the impact of a collection of documents and to determine the structure of a given DL user community. We discuss a system that we have developed to automatically generate extensive journal and document networks from an efficient and simple analysis of user retrieval sequences registered in a particular DL's server logs.

Evaluation of Digital Library Impact and User Communities by Analysis of Usage Patterns

The peer-review process is the most widely accepted certification mechanism for officially accepting the written results of researchers within the scientific community. An essential component of peer-review is the identification of competent referees to review a submitted manuscript. This article presents an algorithm to automatically determine the most appropriate reviewers for a manuscript by way of a co-authorship network data structure and a relative-rank particle-swarm algorithm. This approach is novel in that it is not limited to a pre-selected set of referees, is computationally efficient, requires no human-intervention, and, in some instances, can automatically identify conflict of interest situations. A useful application of this algorithm would be to open commentary peer-review systems because it provides a weighting for each referee with respects to their expertise in the domain of a manuscript. The algorithm is validated using referee bid data from the 2005 Joint Conference on Digital Libraries.

Johan Bollen

Papers

Usage Bibliometrics

The happiness paradox: your friends are happier than you

Usage impact factor: The effects of sample characteristics on usage-based impact metrics

Evaluation of Digital Library Impact and User Communities by Analysis of Usage Patterns

An algorithm to determine peer-reviewers