Understanding Twitter Use by Major LIS Professional Organisations in the United States

Article

Understanding Twitter use by major LIS professional organisations in the United States

Min Zhang

School of Computer and Information Science, Southwest University, China

Feng-Ru Sheu

University Libraries, Kent State University, USA

Yin Zhang

School of Library & Information Science, Kent State University, USA

Abstract

Journal of Information Science

2018, Vol. 44(2) 165–183

The Author(s) 2017 Reprints and permissions: sagepub.co.uk/journalsPermissions.navDOI: 10.1177/0165551516687701journals.sagepub.com/home/jis

Although Twitter has been widely adopted by professional organisations, there has been a lack of understanding and research on its uti-lisation. This article presents a study that looks into how five major library and information science (LIS) professional organisations in the United States use Twitter, including the American Library Association (ALA), Special Libraries Association (SLA), Association for Library and Information Science Education (ALISE), Association for Information Science and Technology (ASIS&T) and the iSchools. Specifically explored are the characteristics of Twitter usage, such as prevalent topics or contents, type of users involved, as well as the user influence based on number of mentions and retweets. The article also presents the network interactions among the LIS associa-tions on Twitter. A systematic Twitter analysis framework of descriptive analytics, content analytics, user analysis and network analytics with relevant metrics used in this study can be applied to other studies of Twitter use.

Keywords

ALA; ALISE; ASIS&T; iSchools; LIS professional organisations; SLA; Social media outreach; Twitter analysis; Twitter use

1. Introduction

Social networking sites, also known as social media, are platforms allowing users to create personal profiles and to con-nect and interact with other users in real time. Examples of such social networking sites include Facebook, Instagram, Flickr and Twitter. Launched in 2006, Twitter has become one of the fastest growing and widely used of these services, free of charge to any individual or group with their account tied to an email address. Twitter allows users to communicate using short messages, up to 140 characters, called ‘tweets’. These messages can contain text, URLs and media such as photos, audio and video. Users can repost others’ messages as ‘retweets’ (RT) or modified ‘retweets’ (MT). Public mes-sages are searchable on the service and can be thematically connected by topical terms called hashtags (#) and directed to users with username mentions (@). This allows users to connect topics and construct conversations with others who share their interests, without necessarily having personal relationships. Given these features, Twitter has become an effective and cost-efficient marketing tool.

Like other fields, library and information science (LIS) professionals are creating institutional and individual Twitter accounts for marketing, providing and promoting patron services and other professional communications. Affordable and easy to manage, Twitter provides new opportunities for libraries and institutions to reach out to their patrons directly

Corresponding author:

Yin Zhang, School of Library & Information Science, Kent State University, Kent, OH 44240, USA.

Email:

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and instantly; it easily conveys information about the library’s hours, events, services and resources [1,2]. Twitter has been used for social networking between libraries and patrons and among information professionals [3,4]. Professional organisations in the field of LIS use Twitter in a similar fashion. However, such use is still a relatively new area for study, unlike use by individuals, businesses and libraries. This study explores the use of Twitter by major LIS profes-sional organisations in the United States. The findings provide insights on how LIS organisations use Twitter and can help librarians and information scientists better understand the trends and the use of Twitter between major LIS organi-sations. As one of few studies first analysing Twitter use by professional organisations with multiple analysis approaches (descriptive analysis, content analysis and network analysis), the research design used in this article provides perspec-tives for the LIS research community in a broader sense.

2. Literature review

2.1. The role of professional associations

A professional association is a body of persons engaged in the same profession, formed usually to control entry into the profession, maintain standards and represent the profession in discussion with other bodies [5]. The LIS profession is sup-ported by a diverse range of professional associations, with each of these organisations providing services and resources for information professionals and advocacy and leadership for the development and delivery of library and information services [6]. LIS associations exist at the international, national, regional, state and local levels; these groups all support practitioners and members with needs, ranging from general to specific, in the area of LIS [7]. This article focuses on the national-level organisations in the United States.

In general, the goals of these associations include the maintenance and development of members’ interests and pro-fessional skills met through offering continuing professional development, training research and education. Other objec-tives include the dissemination of professional news and up-to-date research and trends, the provision of networking and communication opportunities among members and the organisation and providing advocacy for the profession. LIS organisations face many challenges while attempting to reach these goals. According to Madden’s review [8], six com-mon challenges that LIS organisations face include the following:

1. Attracting and retaining members [9];

1. Marketing and promoting services [10];

1. Obtaining and generating funding [11];

1. Keeping information and research up-to-date for members [12];

1. Assessing the effectiveness of provided services [13];

1. Developing information policy and strategies [14].

Twitter is a powerful tool for disseminating information and building online communities for any organisation and is expected to be an effective platform for LIS associations to accomplish such primary goals as providing members with networking and professional development opportunities and in advocating for the profession.

2.2. Twitter use in LIS

Given the increasing popularity and adoption of social networking sites by libraries and institutions, social media studies have become a field of interest for researchers and LIS practitioners. According to Singh and Gill [15], there were more than 200 articles on Web 2.0 technology (including social networking sites and blogs) published in 13 leading LIS journals between 2007 and 2011, making it a trending topic for a growing number of publications. These studies explored the implementation of social media tools, including Twitter, by libraries in various contexts (i.e. public libraries, academic libraries and academic institutions) and from various angles, including why and how libraries use social media [16–20], the impact of social media [21], identifying connecting users [22,23] and social media policies and guidelines [24].

Twitter is ‘social’ media, not a one-way marketing tool. It can be used to learn about customers/patrons and for getting feedback through their tweeted ‘conversations’. As indicated earlier, a growing coterie of professional organisations and libraries use Twitter in a variety of ways.

Journal of Information Science, 44(2) 2018, pp. 165–183The Author(s), DOI: 10.1177/0165551516687701

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2.3. Research tools for Twitter studies

As Twitter studies become an emerging research area, data collection and analysis methods are evolving. In general, there are three major sources for obtaining Twitter data: Twitter search, Twitter application programming interfaces (API) and commercial vendors:

1. Twitter search. Researchers can conduct searches on the site ( using keywords or hash-tags. It allows you to retrieve up to 7 days of historical data or 1500 tweets [25]. These search results can be cop-ied manually or downloaded using an online add-on application such as NCapture, a web browser extension to capture content like web pages and social media for analysis in NVivo.

1. Twitter API. Researchers can use programmes to automatically retrieve Twitter data directly from the site through an API. There is also a plug-in with NodeXL for Microsoft Excel that allows users to explore network graphs. However, according to Kim et al. [25], the number of tweets retrievable through this method is capped at approxi-mately 1% of all tweets.

1. Commercial vendor. Researchers can also purchase full Twitter data, including historical data, through commer-cial vendors who also provide applications that allow for real-time analysis of data. Each source presents unique challenges and values (for comparison of cost and attributes, see Kim et al. [25]).

Previous studies have used a number of analytical methods to gain a better understanding of Twitter data. However, common Twitter analytical frameworks include descriptive analysis, content analysis and network analysis [26]. While descriptive analysis is essential to give an overview of a data set, content analysis is the most common method used in Twitter studies [27]. Given the text-based, user-provided nature of Twitter data, content analysis naturally becomes one of the major analytic methods, allowing researchers to investigate research questions such as user involvement, topics discussed and types and intentions of use. It allows researchers to examine trends, characteristics, patterns, the typology of content in various perspectives and so on. Content analysis can be carried out manually (manual coding based on a set of classifications/themes) or automatically (data mining) by software. Similarly, as Twitter is one of the major social networking sites, network analysis logically suits research needs and becomes another major analytical method to exam-ine the often large data sets generated by users directly, such as their contributed tweets, as well as indirectly, such as the networks formed by their interactions on Twitter. It allows researchers to visualise and reveal the interactions and dynamics between users.

2.4. Research questions

As discussed, the Twitter analytics framework combines three primary analytic techniques, each intended to examine data from different metrics. This study follows this analytics framework to explore the Twitter use of five national-level LIS professional associations based in the United States. This study specifically explores the characteristics of these orga-nisations’ Twitter usage and interactions and is driven to answer the following research questions:

•What are the characteristics of LIS association tweets?

•What topics or concerns do these LIS association tweets share?

•Who are the Twitter users involved in the LIS associations’ tweets?

•What are the interactions among the LIS associations on Twitter?

1. Method

3.1. Sample of LIS professional organisations

Five major US LIS professional organisations included in this study are as follows:

•American Library Association (ALA);

•Special Libraries Association (SLA);

•Association for Information Science and Technology (ASIS&T);

•Association for Library and Information Science Education (ALISE);

•The iSchools, a consortium of information schools.

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The first four are well-established organisations that represent a broad range of the LIS field [28]. ALA is the largest and oldest library association in the world; SLA serves information professionals working in such settings as corpora-tions, the law, academic institutions and government; ASIS&T serves both information science researchers and profes-sionals alike; and ALISE focuses on LIS education. Although established in 2005, iSchools has emerged as a major force for advancing the information field in the 21st century. All five organisations are active on Twitter. It should be noted that although these organisations were initially incorporated in the United States, over time, they have extended their memberships worldwide.

3.2. Data collection and analysis

The researchers utilised NCapture to capture data from Twitter accounts. NCapture (a web browser extension) was used to obtain tweets from the Twitter accounts of these five LIS organisations every 1 or 2 days during a 2-month period from 12 October to 12 December 2015, capturing a total of 15,518 tweets. Table 1 shows a range of basic descriptive statistics of the sample data set, including the number of tweets, distribution of different types, number of hashtags and number of tweets containing URLs and so on.

Twitter data are primarily texts, ‘unstructured’ or ‘semi-structured’ in nature and composed of a short list of words, hashtags, URLs and other information. It is necessary to use content analytics, which refers to a broad set of natural lan-guage processing and text mining methods, for extracting intelligence from Twitter data. This study conducted a content analysis of themes and hashtags to explore the subject matter of LIS professional organisations’ tweets.

NVivo 11 Pro (NVivo for short, subsequently), the latest version of a qualitative and mixed-methods data analysis tool, was used for processing and analysing data. Data from the preliminary descriptive statistical analysis using NVivo were also converted to a Microsoft Excel spreadsheet and imported to Access for further analysis. For qualitative analy-sis, summative content analysis began with identifying themes and hashtags. Major topics were identified by a combina-tion of automatic text mining using NVivo’s auto-themes analysis function and manual categorisation by two authors of this article who followed a two-step approach. They first conducted independent categorisation of the NVivo-generated themes and then met to resolve any differences until all categories were agreed upon as consistent.

The usernames specifically mentioned and retweeted by the five organisations were extracted from tweets to identify the ‘most visible’ or ‘influential’ users. Data were exported in comma-separated values (CSV) format from the spread-sheet and then imported into the Gephi programme in order to visualise the social network embodied in the data.

3.3. Limitations

The intended tweets to be examined were those that originated from or were retweeted by the associations’ accounts. Ideally, all tweets associated with each account should be included. However, the number of tweets that can be captured and the time frame in a given sample are randomly determined by Twitter, and the function of the Twitter application itself, for the account-based data capture request. Variables may include the amount of user traffic and the number of available tweets for an account when captured. As shown in Table 1, the number of ALA tweets in the sample accounts for 24% of that account’s total tweets, while the tweets captured for ALISE account for nearly 100% of their total. Despite this limitation, given the exploratory and descriptive nature of this study, we believe that using the maximum number of captured tweets from all accounts would be more helpful to shed light on the Twitter use of these organisa-tions as a whole, rather than a smaller subset of tweets.

4. Results

4.1. Descriptive analytics

As shown in Table 1, among the 15,518 tweets, 60% (9312) were original and 40% (6206) were RT. Over 55% (8590) of tweets contained at least one hashtag, and 2354 different hashtags were captured. The ratio of an account’s tweets con-taining at least one hashtag ranged from 54% (iSchools) to 65% (ALA). This result suggests that most of these tweets were topical in nature. URLs were popular tweet content, with 82% of sampled tweets containing at least one. Here, the ratios ranged from the highest of 92% of iSchools’ tweets to the lowest 66% of ALISE’s. In addition, 44% of all tweets collected mentioned at least one user. ASIS&T had the most conversational or interactive tweets, with 61% of theirs con-taining usernames. Conversely, SLA appeared the least conversational or interactive, as only 38% of their tweets men-tioning users.

Journal of Information Science, 44(2) 2018, pp. 165–183The Author(s), DOI: 10.1177/0165551516687701

Journal of Information Science, 44(2) 2018, pp. 165–183 The Author(s), DOI: 10.1177/0165551516687701

Table 1. Number of tweets captured from five LIS associations and basic descriptive statistics

Metric / ALA / SLA / ASIS&T / ALISE / iSchools / Total
Number of followers / 75,400 / 4488 / 4668 / 586 / 2927 / 88,069
Number of tweets in account / 14,000 / 11,500 / 7286 / 1465 / 3781 / 38,032
history
Number of all types of tweets / 3330 / 4041 / 3360 / 1462 / 3325 / 15,518
captured in the sample (see
breakdown in next two rows)
Number of original tweets / 2024 / 2744 / 2844 / 647 / 1053 / 9312
Number of retweets / 1306 / 1297 / 516 / 815 / 2272 / 6206
Number of tweets with hashtags / 2164 / 2122 / 1712 / 790 / 1802 / 8590
Number of hashtags / 790 / 279 / 619 / 368 / 735 / 2354 (unique)
Number of tweets with links / 2617 / 3448 / 2692 / 965 / 3045 / 12,767
Number of tweets with / 1369 / 1517 / 2058 / 598 / 1336 / 6878
mentions
Account creation dates / February 2009 / March 2009 / August 2009 / December 2012 / January 2009
Time frame coverage of tweets / 14 April 2014–12 / 10 April 2015–12 / 6 June 2013–11 / 5 December 2012–11 / 1 December 2010–10
in sample / December 2015 / December 2015 / December 2015 / December 2015 / December 2015

LIS: library and information science; ALA: American Library Association; SLA: Special Libraries Association; ASIS&T: Association for Information Science and Technology; ALISE: Association for Library and Information Science Education.

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Table 2. The theme categories of five LIS professional organisations’ tweets
Theme category / Frequency / Percentage
Libraries (all types) & Services / 877 / 27
Research / 546 / 17
Conferences/Webinars/Continuing Education / 528 / 16
Information Concepts / 374 / 12
LIS Education / 358 / 11
Librarians and Jobs (all LIS career/job related) / 345 / 11
Social Media (Web 2.0 resource related) / 196 / 6