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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.

This research aims to analyze academic libraries’ Twitter content and present a categorization framework for the study of their tweets.

The research adopted a statistical descriptive analysis in addition to a content analysis of the tweets. Consequently, many categories and subcategories were created to classify the tweets according to different aspects. A total of 17 academic library accounts were examined.

The findings show that academic libraries used Twitter as a multifaceted tool. “News and announcements” received the highest score as the type of information most often posted on Twitter by libraries, followed by “library collections” and “library services”. The subcategories that received the highest scores were “library marketing and news”, “answers and referrals” and “books”. Academic libraries showed a penchant for posting links more often than other content. Other results show different patterns of communication and interaction between libraries and their Twitter followers.

The categorization of tweets provides a framework for understanding how academic libraries use Twitter.

As little research can be found in the library and information science literature on Twitter content analysis, this research is expected to contribute significantly. It will also support academic libraries and librarians in using Twitter more efficiently and effectively.

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.

The main aim of this paper is to build an approach to analyze the tourist content posted on social media. The approach incorporates information extraction, cleaning, data processing, descriptive and content analysis and can be used on different social media platforms such as Instagram, Facebook, etc. This work proposes an approach to social media analytics in traveler-generated content (TGC), and the authors use Twitter to apply this study and examine data about the city and the province of Granada.

In order to identify what people are talking and posting on social media about places, events, restaurants, hotels, etc. the authors propose the following approach for data collection, cleaning and data analysis. The authors first identify the main keywords for the place of study. A descriptive analysis is subsequently performed, and this includes post metrics with geo-tagged analysis and user metrics, retweets and likes, comments, videos, photos and followers. The text is then cleaned. Finally, content analysis is conducted, and this includes word frequency calculation, sentiment and emotion detection and word clouds. Topic modeling was also performed with latent Dirichlet association (LDA).

The authors used the framework to collect 262,859 tweets about Granada. The most important hashtags are #Alhambra and #SierraNevada, and the most prolific user is @AlhambraCultura. The approach uses a seasonal context, and the posted tweets are divided into two periods (spring–summer and autumn–winter). Word frequency was calculated and again Granada, Alhambra are the most frequent words in both periods in English and Spanish. The topic models show the subjects that are mentioned in both languages, and although there are certain small differences in terms of language and season, the Alhambra, Sierra Nevada and gastronomy stand out as the most important topics.

Extremely difficult to identify sarcasm, posts may be ambiguous, users may use both Spanish and English words in their tweets and tweets may contain spelling mistakes, colloquialisms or even abbreviations. Multilingualism represents also an important limitation since it is not clear how tweets written in different languages should be processed. The size of the data set is also an important factor since the greater the amount of data, the better the results. One of the largest limitations is the small number of geo-tagged tweets as geo-tagging would provide information about the place where the tweet was posted and opinions of it.

This study proposes an interesting way to analyze social media data, bridging tourism and social media literature in the data analysis context and contributes to discover patterns and features of the tourism destination through social media. The approach used provides the prospective traveler with an overview of the most popular places and the major posters for a particular tourist destination. From a business perspective, it informs managers of the most influential users, and the information obtained can be extremely useful for managing their tourism products in that region.

Healthy food sales have increased in recent decades. Retailers are widening their marketing management approach, including the use of social media to communicate with consumers and to promote healthy food. The purpose of this paper is to investigate European retailers’ social media communication content used to promote healthy food products, by analysing retailers’ Twitter messages and accounts characteristics, retailers’ Twitter messages content on healthy food and retailers’ Twitter accounts orientation on healthy food.

Data include approximately 74,000 tweets sent in 2016 from 90 corporate and brand accounts. The tweets were sent by the top 36 European retailers. Data elaboration includes quantitative content analysis of Twitter messages, which is used to identify healthy food categories’ occurrences and co-occurrences. Then, multiple multivariate-linear regression analyses explore the relation between retailers’ characteristics and healthy food messaging and between the overall content of retailer accounts and a healthy food focus.

The vast majority of retailers’ tweets on healthy food issues mainly address general health and sustainability issues. Tweets about food health and nutrition refer to food types, meals or consumer segments. Tweets about food sustainability refer to general issues. Analysis of retailer accounts shows that the larger the retailer is, the lower the relevance of healthy food. Retailers with high numbers of tweets and followers tend to decrease their attention to healthy food promotion. Compared to retailers with lower revenues, retailers with higher revenues tend to send a higher number of tweets that focus on healthy food but the incidence is lower compared to the overall accounts’ messaging.

As the study focuses on a single category of food products, further research into other categories of retail products may contribute to a wider perspective. Future research may include graphical content/emoticons and extend the analysis to other social media platforms. Finally, social media data allow studies to cover a wide geographical area. However, in order to also value non-English written messaging, this research introduces some approximations in language interpretation.

The research provides insights into how retailers use social media and provides an overview of how retailers manage their social media communication in one of the most promising food product categories. Retailers manage social media communication content cautiously to minimise controversial issues. This study provides insights into the need to more effectively target the increasing number of social media users.

The research approach and findings of this study extend prior research on retailers’ communication management by improving the understanding of retailers’ use of social media and marketing communication content for their key products, focusing on healthy food.

The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India.

The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India.

The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India.

The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue.

Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

The purpose of this paper is to thematically categorise sports sponsorship-linked Twitter content and, by drawing on uses & gratifications theory, to map the extent to which these categories cohere with known user motivations for consuming social media.

Qualitative content analysis of a sample of 1,502 tweets by London 2012 Olympic and Paralympic Games sponsors posted between January 2011 and September 2012 was used to develop the typology of sponsorship-linked Twitter content.

From the data, a typology is developed, comprising 17 categories grouped under four main types: informing, entertaining, rewarding and interacting. The majority of sponsor tweets (68 per cent) fell into the informing type, with 17 per cent categorised as interacting. While few (2 per cent) tweets were categorised as entertaining, the link to the sponsored event implies a degree of entertaining content even in ostensibly informative, rewarding or interactional sponsorship-linked tweets. Therefore, the typology categories highlight Twitter content produced by sponsors which engages customers, fostering dialogue alongside providing informative and entertaining content.

The typology can inform practitioners’ future sports sponsorship activation planning decisions and can also aid rights holders in tailoring appropriate sponsorship opportunities to potential sponsors, based on an appreciation of the nature of content sought by brand followers.

The typology extends existing understanding of the use of social media within sponsorship activation campaigns by thematically categorising content and mapping this against known user motivations for consuming brand-related social media content.

The purpose of this paper is to propose a robust framework for assessing motivations to use Twitter. Drawing on previous studies, diffusion of innovations, unified theory of acceptance and use of technology, and uses and gratifications frameworks were incorporated to capture the variety in Twitter gratifications.

This study explored the motivations to use Twitter through data mining and content analysis of #IUseTwitterBecause trending topic in Turkey. The sample was consisted of 4,465 tweets posted by 3,794 unique Twitter persona.

Findings revealed 13 distinct Twitter gratifications which were grouped under four main categories: content, process, social and technology. Process gratifications were found to be the strongest motives for using Twitter whereas content gratifications were the weakest. The current findings indicated that Turkish users engage with Twitter mostly for self-expression, escapism, and social interaction. Moreover, the study found a novel campaign gratification for using Twitter solely to make political or social agenda visible in the society.

The current study extends the previous literature on motivations in using social networking services from both analytical and methodological perspectives. The naturally occurring data and relatively large sample size in this study provides a deeper look into social media gratifications and yields more generalizable findings. Besides, this study contributes to the alleviation of conceptual confusions among the past social media gratification studies.

This study aims to investigate motivations and human values of everyday consumers who participate in the annual day of consumption restraint known as Buy Nothing Day (BND). In addition, this study demonstrates a hybrid content analysis method in which artificial intelligence and human contributions are used in the data analysis.

This research uses a hybrid method of content analysis of a large Twitter data set spanning three years.

Consumer motivations are categorized as relating to consumerism, personal welfare, wastefulness, environment, inequality, anti-capitalism, financial responsibility, financial necessity, health, ethics and resistance to American culture. Of these, consumerism and personal welfare are the most common. Moreover, human values related to “openness to change” and “self-transcendence” were prominent in the BND tweets.

This research demonstrates the effectiveness of a hybrid content analysis methodology and uncovers the motivations and human values that average consumers (as opposed to consumer activists) have to restrain their consumption. This research also provides insight for firms wishing to better understand and respond to consumption restraint.

This research provides insight for firms wishing to better understand and respond to consumption restraint.

The question of why everyday consumers engage in consumption restraint has received little attention in the scholarly discourse; this research provides insight into “everyday” consumer motivations for engaging in restraint using a hybrid content analysis of a large data set spanning over three years.

The purpose of this paper is to explore how the dialogic theory of public relations can help strategic communication practitioners support and enhance the relationship between individuals and organizations.

This inquiry applied the dialogic theory of public relations by investigating leading media companies’ context-based strategic use of Twitter. Researchers conducted a qualitative content analysis of 1,800 tweets from 18 top-performing media organizations.

This study identified strategies, rooted in dialogic theoretical principles that media organizations used to engage stakeholders. Media companies employed strategies based on dialogic principles, including promoting organizations as industry and thought leaders, integrating social media, and using an interactive, synergistic organizational voice.

These strategies support the need to expand theoretical conceptualizations and use of dialogic principles to study online communication.

Findings offer practical strategies for practitioners managing organizations’ Twitter communication to foster engagement. In particular, practitioners should consider organizational context and subsequent content advantages.

Findings offer practical and theoretical contributions to the debate of interactivity.