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This chapter provides an overview of the specific legal, ethical, and privacy issues that can arise when conducting research using Twitter data. Existing literature is reviewed to inform those who may be undertaking social media research. We also present a number of industry and academic case studies in order to highlight the challenges that may arise in research projects using social media data. Finally, the chapter provides an overview of the process that was followed to gain ethics approval for a Ph.D. project using Twitter as a primary source of data. By outlining a number of Twitter-specific research case studies, the chapter will be a valuable resource to those considering the ethical implications of their own research projects utilizing social media data. Moreover, the chapter outlines existing work looking at the ethical practicalities of social media data and relates their applicability to researching Twitter.

Organization development is often mourned as stagnant or perhaps dead, but most of these declarations seem to be insular, being supported primarily by anecdotal or survey research among organization development scholars and practitioners. This exploratory study seeks a more objective understanding of the state of organization development by examining big data from the social media platform Twitter. Drawn from over 5.7 million tweets extracted through Twitter's Application Program Interface (API) during 2 months in 2018, this research approaches the state of organization development through a quantitative, abductive study utilizing social network analyses. Organization development is examined through its characteristics as a social network on Twitter and how it relates to and interacts with other familial networks from management and organization studies. Findings show that organization development is relatively inactive as a social network on Twitter, as compared to other familial networks, and the relationships between the organization development network and these familial networks tend to be ones of inequality. Organization development references familial networks much more than any of the familial networks reference organization development. This inequality in social media presence is particularly surprising since several of these familial networks were founded from the field and principles of organization development. We locate organization development's generalist status, as compared to familial networks' specialist status, as generating this interaction disparity drawing on recent research that suggests specialized fields fare better in times of rapid change compared to generalist fields. We discuss the potential for greater specialization of organization development with a reemphasis on its process philosophy and focus.

This paper aims to estimate the population in a specific space from the numbers of posted tweets and their senders, using Twitter's real-time property and location information data.

The population to be estimated was set to be the attendance at each game among the six baseball teams of the Japan Professional Baseball Pacific League held at the main stadium of each team. The relation between the attendance and Twitter data was analyzed, and regression models using Twitter data were used to estimate the attendances.

The correlation coefficient tended to be larger for the attendance and tweeting users than for the attendance and that of the number of tweets. Furthermore, the comparison and evaluation of several regression models combining Twitter data, game data and weather data for estimating the attendance showed the usefulness of Twitter data, and that using the number of tweeting users improved the accuracy of population estimation.

While there are many studies on event detection or location identification using Twitter data, no study has been reported on the estimation of the population in a specific space using “time information” and “location information” characteristic of Twitter data. Using Twitter data, which contains users' messages, for estimating the population can be extended to various types of analyses, such as the analysis of feelings and opinions of the groups in the space.

The purpose of this paper is to highlight the importance of understanding, analyzing and managing big data in the field of public administration.

It discusses the unique attributes of Twitter data and proposes a text-mining and analysis framework for conducting research using data on social networking sites. Furthermore, this paper reviews recent scholarship that collected and analyzed Twitter data to address public administration or policy issues. It examines what research questions have been addressed through analyzing Twitter data and how researchers collected and analyzed Twitter data.

It concludes that a more systematic process is needed for data collection, and more in-depth analysis is needed to better understand and utilize Twitter data for public administration.

This paper is one of the early attempts to synthesize and review the existing research that used Twitter data to address public administration issues.

Social media is noted for its usefulness and contribution to destination marketing and management. Social media data is particularly valued as a source to understand issues such as tourist behavior and destination marketing strategies. Among the social media platforms, Twitter is one of the most utilized in research. Its use raises two issues: the challenge of obtaining historical data and the importance of qualitative data analysis. Regarding these issues, the chapter argues that retrieving tweets using hashtags and keywords on the Twitter website provides a corpus of tweets that is valuable for research, especially for qualitative inquiries. In addition, the value of qualitative analysis of Twitter data is presented, demonstrating, among other things, how such an approach captures in-depth information, enables appreciation and inclusion of the nonconventional language used on social media, distinguishes between “noise” and useful information, and recognizes information as the sum of all parts in the data.

The focus of this chapter is the intersection of social media, publication, data sharing, and research ethics. By now there is an extensive literature on the use of social media in research. There is also excellent work on challenges of postpublication sharing of social media, primarily focused on legal restrictions, technical infrastructure, and documentation. This chapter attempts to build upon and extend this work by using cases to deepen the analysis of ethical issues arising from publishing and sharing social media data. Publishing will refer to the presentation of data extracts, aggregations, or summaries, while sharing refers to the practice of making the underlying data available postpublication for others to use. It will look at the ethical questions that arise both for researchers (or others) sharing data, and those who are using data that has been made available by others, emphasizing the inherently relational nature of data sharing. The ethical challenges researchers face when considering sharing user-generated content collected from social media platforms are the focus of the cases. The chapter begins by summarizing the general principles of research ethics, then identifies the specific ethical challenges from sharing social media data and positions these challenges in the context of these general principles. These challenges are then analyzed in more detail with cases from research projects that drew upon several different genres of social media. The chapter concludes with some recommendations for practical guidance and considers the future of ethical practice in sharing social media data.

The United Nations (UN) actively incorporated new media as a tool for consultation and agenda setting during the Millennium Development Goal (MDG)–Sustainable Development Goal (SDG) transition. As global actors shifted their attention to the sustainable development goals, the UN and its partners scaled up their digital engagement with civil society, multinational agencies, and country-level stakeholders to inform the post-2015 agenda. This chapter explored how the UN integrated Twitter into the post-2015 consultation and how the UN Women and the United Nations Girls’ Education Initiative used Twitter to construct and diffuse girls’ education policy discourse during the MDG–SDG transition.

It is difficult to build our own social data set because data in social media is generally too vast and noisy. The aim of this study is to specify design and implementation details of the Twitter data collecting tool with a rule‐based filtering module. Additionally, the paper aims to see how people communicate with each other through social networks in a case study with rule‐based analysis.

The authors developed a java‐based data gathering tool with a rule‐based filtering module for collecting data from Twitter. This paper introduces the design specifications and explain the implementation details of the Twitter Data Collecting Tool with detailed Unified Modeling Language (UML) diagrams. The Model View Controller (MVC) framework is applied in this system to support various types of user interfaces.

The Twitter Data Collecting Tool is able to gather a huge amount of data from Twitter and filter the data with modest rules for complex logic. This case study shows that a historical event creates buzz on Twitter and people's interests on the event are reflected in their Twitter activity.

Applying data‐mining techniques to the social network data has so much potential. A possible improvement to the Twitter Data Collecting Tool would be an adaptation of a built‐in data‐mining module.

This paper focuses on designing a system handling massive amounts of Twitter Data. This is the first approach to embed a rule engine for filtering and analyzing social data. This paper will be valuable to those who may want to build their own Twitter dataset, apply customized filtering options to get rid of unnecessary, noisy data, and analyze social data to discover new knowledge.

This paper aims to perform an exhaustive revision of relevant and recent related studies, which reveals that both extraction methods are currently used to analyze credibility on Twitter. Thus, there is clear evidence of the need of having different options to extract different data for this purpose. Nevertheless, none of these studies perform a comparative evaluation of both extraction techniques. Moreover, the authors extend a previous comparison, which uses a recent developed framework that offers both alternates of data extraction and implements a previously proposed credibility model, by adding a qualitative evaluation and a Twitter-Application Programming Interface (API) performance analysis from different locations.

As one of the most popular social platforms, Twitter has been the focus of recent research aimed at analyzing the credibility of the shared information. To do so, several proposals use either Twitter API or Web scraping to extract the data to perform the analysis. Qualitative and quantitative evaluations are performed to discover the advantages and disadvantages of both extraction methods.

The study demonstrates the differences in terms of accuracy and efficiency of both extraction methods and gives relevance to much more problems related to this area to pursue true transparency and legitimacy of information on the Web.

Results report that some Twitter attributes cannot be retrieved by Web scraping. Both methods produce identical credibility values when a robust normalization process is applied to the text (i.e. tweet). Moreover, concerning the time performance, Web scraping is faster than Twitter API and it is more flexible in terms of obtaining data; however, Web scraping is very sensitive to website changes. Additionally, the response time of the Twitter API is proportional to the distance from the central server at San Francisco.

The purpose of this paper is to engage in a systematic analysis of academic research that relies on the collection and use of Twitter data, creating topology of Twitter research that details the disciplines and methods of analysis, amount of tweets and users under analysis, the methods used to collect Twitter data, and accounts of ethical considerations related to these projects.

Content analysis of 382 academic publications from 2006 to 2012 that used Twitter as their primary platform for data collection and analysis.

The analysis of over 380 scholarly publications utilizing Twitter data reveals noteworthy trends related to the growth of Twitter-based research overall, the disciplines engaged in such research, the methods of acquiring Twitter data for analysis, and emerging ethical considerations of such research.

The findings provide a benchmark analysis that must be updated with the continued growth of Twitter-based research.

The research is the first full-text systematic analysis of Twitter-based research projects, focussing on the growth in discipline and methods as well as its ethical implications. It is of value for the broader research community currently engaged in social media-based research, and will prompt reflexive evaluation of what research is occurring, how it is occurring, what is being done with Twitter data, and how researchers are addressing the ethics of Twitter-based research.