Search results

The purpose of this paper is to investigate the types of roles that Twitter played in digital libraries (DLs) and their relationships in building DL online communities.

A mixed method analysis of DLs’ tweets was conducted to identify the usage, roles of Twitter in DL communities and relationships among the roles. Twitter data from 15 different DLs for one year were extracted, and an open coding analysis was performed to identify types of Twitter roles. Pearson correlation coefficient was applied to examine the relationships among the roles based on word similarities.

The results present 15 types of Twitter roles representing five main categories identified from DL tweets, including information, promotion, related resources, social identity and social connection. Moreover, word similarities analysis identifies more strong relationships among the roles in four main categories (promotion, related resources, social identity and social connection) but less with roles in information.

Characteristics of DL online communities are discussed and compared with physical library communities. Suggestions are proposed for how tweets can be improved to play more effective roles. To build a strong community, it is critical for digital librarians to engage with followers.

This study is a pioneering work that not only analyzes Twitter roles and their relationships in building DL online communities but also offers recommendations in terms of how to build a strong online community and improve Twitter use in DLs.

The purpose of this paper is to design organization message content strategies and analyse their information diffusion on the microblogging website, Twitter.

Using data from 29 brands and 9392 tweets, message strategies on twitter are classified into four strategies. Using content analysis all the tweets are classified into informational strategy, transformational strategy, interactional strategy and promotional strategy. Additionally, the information diffusion for the developed message strategies was explored. Furthermore, message content features such as text readability features, language features, Twitter-specific features, vividness features on information diffusion are analysed across message strategies. Additionally, the interaction between message strategies and message features was carried out.

Finding reveals that informational strategies were the dominant message strategy on Twitter. The influence of text readability features language features, Twitter-specific features, vividness features that influenced information diffusion varied across four message strategies.

This study offers a completely novel way for effectively analysing information diffusion for branded tweets on Twitter and can show a path to both researchers and practitioners for the development of successful social media marketing strategies.

Based on social conformity and social value perspectives, the authors aim to empirically examine the critical role of social influences on Twitter user behaviour.

An integrative structural model is developed to explain how social influences, which are divided into social conformity and social values, work with other salient factors such as the utilitarian and hedonic values to affect the use of Twitter. The partial least square method was used to verify the proposed model with usable data from 204 adult Korean Twitter users.

Social conformity positively affects the frequent use of Twitter and it is mediated through perceived values such as the hedonic, utilitarian and social appearance values associated with Twitter use. While social appearance negatively affected the trustworthiness of information shared on Twitter, the social capital effect on Twitter use was insignificant.

This study has some limitations for general applicability of the results: the samples include only adult users in Korea, and it is a cross-sectional study for a specific social networking service. This study theoretically disentangles the complicated nature of social influences by proposing two distinct constructs (social conformity and social values) and empirically verifying their significant roles in Twitter use.

Practitioners should recognise that the hedonic value of Twitter is the key factor that affects both the quantity and quality of information shared on Twitter. In addition user motivations for pursuing social appearance in SNSs might cause a user to distort the information that they produce.

This study provides unique implications related to the role of social conformity, social values and user behaviour on Twitter.

The use of Twitter by political parties and politicians has been well studied in developed countries. However, there is a lack of empirical work, which has examined the use of Twitter in developing countries. This study aims to explore the information-sharing patterns of Pakistani politicians through Twitter accounts during the pre-election campaign of 2018.

Data of three weeks of the official party accounts and the politicians running for prime minister were analysed. The mixed-methods approach has been used to analyse quantitative and qualitative data retrieved through Twitonomy.

It was found that the most active Twitter account belonged to the winning party. The prominent Twitter account functions were a call to vote, promotional Tweets, promises and Tweeting about party developments. The present study provides evidence that there is a difference between the Tweeting behaviour of established and emerging parties. The emerging party heavily posted about changing traditional norms/culture/practices.

The study contributed to existing knowledge and has practical implications for politicians, citizens and social media planners.

The present study was designed carefully and based on empirical research. The study is unique in its nature to fill the research and knowledge gap by adding a variety of Twitter functions used by politicians.

American police departments are beginning to implement social media as a strategy to engage the surrounding communities through various methods, including Facebook and Twitter. The purpose of this paper is to examine the varieties in the use of Facebook and Twitter by local police departments.

This study collected all data between October 1, 2013 and March 31, 2014 from Facebook and Twitter accounts of seven city police departments in the Hampton Roads area of Virginia. These agencies resemble many police departments in the USA, and in total serve a diverse population of approximately 1,435,000. Content analysis and statistical tests are conducted.

Results show that specific types of posts are more engaging for the community. Facebook and Twitter interactions vary depending upon the type of posts, demonstrating that citizens are using Facebook and Twitter to interact in different ways.

The findings presented here give police agencies’ insight on how to appropriately adjust their use of social media to fulfill the needs of the citizens and optimize interactions with the community.

This is the first study to systematically examine and analyze the varieties in the use of social media by traditional American local police departments and their interactions with citizens.

Twitter is a popular microblogging service which has proven, in recent years, its potential for propagating news and information about developing events. The purpose of this paper is to focus on the analysis of information credibility on Twitter. The purpose of our research is to establish if an automatic discovery process of relevant and credible news events can be achieved.

The paper follows a supervised learning approach for the task of automatic classification of credible news events. A first classifier decides if an information cascade corresponds to a newsworthy event. Then a second classifier decides if this cascade can be considered credible or not. The paper undertakes this effort training over a significant amount of labeled data, obtained using crowdsourcing tools. The paper validates these classifiers under two settings: the first, a sample of automatically detected Twitter “trends” in English, and second, the paper tests how well this model transfers to Twitter topics in Spanish, automatically detected during a natural disaster.

There are measurable differences in the way microblog messages propagate. The paper shows that these differences are related to the newsworthiness and credibility of the information conveyed, and describes features that are effective for classifying information automatically as credible or not credible.

The paper first tests the approach under normal conditions, and then the paper extends the findings to a disaster management situation, where many news and rumors arise. Additionally, by analyzing the transfer of our classifiers across languages, the paper is able to look more deeply into which topic-features are more relevant for credibility assessment. To the best of our knowledge, this is the first paper that studies the power of prediction of social media for information credibility, considering model transfer into time-sensitive and language-sensitive contexts.

The purpose of this paper is to predict Twitter satisfaction by healthcare professionals through integrating constructs of Csikszentmihalyi’s flow theory, quality dimensions and usefulness.

Survey responses of 108 physicians from a variety of specialisations in the United Arab Emirates have been validated and analysed by means of partial least squares-based structural equation modelling method using smartPLS software.

Service quality has emerged as the most influential quality dimension that positively impact flow state and perceived usefulness of Twitter, while information quality, surprisingly, does not show any effect. The findings also indicate that flow state plays a significant role in shaping physicians’ satisfaction with Twitter. The study also enhances our understanding concerning the effects of perceived usefulness on flow state and satisfaction.

Understanding factors that influence Twitter satisfaction can help healthcare managers construct appropriate intervention strategies for maximising professional benefits of social media and minimising user resistance. This is important because top managers usually ratify traditional practices that are only of limited effect. Also, the findings help vendors to accentuate user’s concerns in addition to system functionalities in social media applications.

The paper is an early attempt to propose a model for social media success in a professional context in general and healthcare in particular. It also one of first studies that examine social media satisfaction through integrating contemporary information system success and acceptance models with flow theory.

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 understand the online communication strategies used by destination management organizations (DMOs) during a terrorist attack. In particular, this study analyzes Twitter use during seven terrorism incidents in six European cities (Paris, Brussels, Nice, Berlin, London and Barcelona) between 2015 and 2017.

Twitter content was collected via NCapture, a web browser extension of NVivo, one week prior to the attacks, the day of, and two weeks following to determine the timeframe in which DMOs communicated about the crisis, the types of messages being communicated, and whether these messages are effective. This study uses Coombs’ Situational Crisis Communication Theory as a guide to analyzing the effectiveness of the crisis communication strategies.

The findings of this paper indicate that DMOs are not effectively using Twitter during a terrorist attack. Few tweets relating to the attacks provided tourists with information regarding their safety, with the remaining only communicating as victims. Many DMOs went offline in the days immediately following the attacks and each DMO’s crisis communication on Twitter only lasted up to one week following the attacks.

This study provides insight into the ways DMOs are using social media for crisis communication. These results inform DMOs on their responsibility in communicating information during a terrorist attack. Messages of support are useful in the recovery stage, but tourists need information on how to stay safe and Twitter is often the first source people go to for information (Simon et al., 2014).

In view of the increased impact of social media, non-profit organizations (NPOs) should review their management model for stakeholder relationships. The purpose of this paper is to analyze the influence of factors on the use of Twitter by NPOs as a mechanism for disclosing information and dialogue with their stakeholders, and in particular: “donor dependence,” “fundraising expenses,” “organizational age,” “organizational size,” “online community size,” “network activity” and “board size.”

A content analysis of Twitter use as a strategic communication tool was conducted and a multivariable linear regression analysis was performed.

Smaller organizations and those with a larger online community are the most interested in implementing Twitter as a one or two-way communication strategy. Likewise, the NPOs with the highest degree of donor dependence strive most in the use of contents of Twitter as one-way communication mechanism.

This study contributes to a better understanding of social media implementation in the NPO sector and advances the identification of the main causes that motivate NPOs to improve their accountability through social media.

The paper’s subject is relatively innovative due to the scarce studies focussing on social media used by NPOs. In spite of the advantages offered by Twitter, few studies have specifically addressed NPOs’ current use of this platform, and fewer still have analyzed the factors that motivate them to use this tool.