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Getting high-quality data by removing the noisy data from the user-generated content (UGC) is the first step toward data mining and effective decision-making based on ubiquitous and unstructured social media data. This paper aims to design a framework for revoking noisy data from UGC.

In this paper, the authors consider a classification-based framework to remove the noise from the unstructured UGC in social media community. They treat the noise as the concerned topic non-relevant messages and apply a text classification-based approach to remove the noise. They introduce a domain lexicon to help identify the concerned topic from noise and compare the performance of several classification algorithms combined with different feature selection methods.

Experimental results based on a Chinese stock forum show that 84.9 per cent of all the noise data from the UGC could be removed with little valuable information loss. The support vector machines classifier combined with information gain feature extraction model is the best choice for this system. With longer messages getting better classification performance, it has been found that the length of messages affects the system performance.

The proposed method could be used for preprocessing in text mining and new knowledge discovery from the big data.

To present YouFlow Microblog and how its functionalities for discourse structuring and message classification allows improving learning. The paper aims to discuss these issues.

The authors developed a survey on microblogs and its functionalities for supporting education, and then the authors developed a new microblog called YouFlow Microblog, which was used in a case study to illustrate its applicability.

The results were: the categorization of messages according to the lesson plan allowed deeper discussions; the amount of messages and preference of the students for the categorization approach are directly related; the filtering of messages (categorization on the reading time) was used as a facilitator of the discussion understanding; the students prefer the search results organized by the categories and related messages; and the students were able to follow the discussions supported by the tree structure of discourse, categorization of messages according to the lesson plan and searches or filtering of messages.

Other case studies involving more students and from different knowledge areas could be performed. Other approaches for messages' visualization for reducing the amount of information could be explored, such as recommendation of messages in microblogs; and groupings of messages according to date, priority and subject.

The developed microblog is useful and proved to be interesting for applying in the educational context.

No other microblog allows different structuring of messages and the their classification according to the lesson plan, in addition to filtering and query functionalities.

Argues that, in order to use electronic data interchange (EDI) optimally, the current ways of working need to be redesigned. Here the subject‐matter for redesign is the boundary‐crossing logistical processes in the value‐adding partnerships of two organizations. The business redesigner needs, among other things, an understanding of the basic capabilities of EDI and of the concept of interorganizational co‐ordination. From the analysis of co‐ordination a classification of the information needed by logistical organizations results. Together with three basic co‐ordination mechanisms and a classification of messages, the classification provides a sound basis of understanding for the business redesigner.

The papers by Robert A. Fairthorne are always both stimulating and challenging to me. Their insights and their obscurities are fascinating. While my own intellectual works have usually been quite separate from his, we have frequently shared a concern with many of the same topics in the principles of information work. These have included such topics as the mathematical basis of classification, applications of mathematical lattice theory, insights from Shannon's signalling theory (habitually misnamed ‘information theory’), and the delegation of retrieval.

Organizations are communicating with the public about their thoughts and behaviors relevant to the environment via social networking sites. The purpose of this paper is to explore for-profit and nonprofit organizations' Twitter messages to understand their environment-related messages and their influences on the publics' responses.

This study conducted a content analysis adopting four message classification systems: environmental message orientation, message specificity, message framing, and environmental issue. Guided by attribution theory, this study also explored how the organization's environmental messages influence social media (Twitter) user responses, likes, retweets, and replies.

The analysis showed that for-profits' messages tend to discuss their green products and manufacturing processes with specific numeric evidence, while nonprofits are disposed to describe a severely degraded environment. In addition, the study revealed that tweets yield a high number of likes and replies when the organizations are for-profits and the messages emphasize green products.

The findings of this study showed that the green message categorization systems are applicable to the social media context. But, this study focused on Twitter only. Future studies need to examine various social media platforms.

The study findings recommend communication practitioners use substantive green messages highlighting actual pro-environmental performances. Also, practitioners might need to make a linkage between the discussed environmental issue and the organization (e.g. a water issue by a wildlife-related nonprofit, an energy issue by a home appliance manufacturer, an air pollution issue by a bicycle company). In addition, regarding the message specificity, infographics can be present specific information that audiences can readily understand because it is described visually.

Scholars investigated environmental messages in advertising and cautioned that environmental messages that are not substantive or specific can cause audiences to perceive the messages as greenwashing. However, these previous studies focused on conventional media, and they have not been replicated in the age of social media. Thus, it is important to explore the current status of organizational environmental messages on social media.

The purpose of this study is to develop a typology of social media messages to compare the effectiveness of different message strategies.

In total, 1,837 messages from 12 hotel brand Facebook pages were content-analyzed. Applying both correspondence analysis and multivariate analysis of variance, the study compared message strategy across hotel-scale levels and explored the effectiveness of different message strategies.

A typology of four-type message format and six-type message content was developed. The picture message was the best message format. Product, brand and involvement messages were shown to be more effective than information, reward and promotion messages. Promotion message was the least effective message content type.

The major limitation of this study is the generalizability owing to the sample selection process. There is also the limitation on exclusion of control variables, selection of the three effectiveness measures and evolving social media technology.

The typology of Facebook message strategy developed in the study provided guidelines for hotel marketers to create messages on Facebook pages and track effectiveness. Hotels should also take full advantage of the picture format and product, brand and involvement contents.

This study created a new typology of social media message strategy consisting of two dimensions. It also provided empirical evidence to support the application of message strategy theory in the hotel social media marketing area.

John and Storr (this volume) make the case that quantitative methods help establish whether culture matters, but do not tell us how culture matters. To better understand how culture matters, social scientists must use qualitative methods like interviews, in-depth case studies, and archival research. Currently, experimental economists engage qualitative methods through the coding of “chat” transcripts and informal talks with subjects while payments are arranged. Experimental economists do this because they know that it is a good idea to talk to the people they seek to understand and learn from their thought process. The goal of this chapter is to build on the insights from John and Storr about the importance of qualitative work and to provide experimental economists with some concrete ideas about qualitative methods that can improve their research.

The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their members increasingly use and misuse various terms to restrict their communication. This includes formal classifications for data security, financial/knowledge management, human resources, and other functions as well as those used informally by organizational members. Especially in a data-rich environment where our word-processing programs, e-mail tools, and other technologies afford us opportunities to engage in classification, a wide range of people at all organizational levels may serve as custodians of their own data and thus have the ability (as well as perhaps the need) to classify messages in various ways. The purpose of this paper is to describe key classification terms ranging from those found in government (e.g. top secret, confidential) to those in the private sector (e.g. business use only, trademarked) to an even wider set of terms used informally by organizational members (e.g. personal, preliminary). The growing use of message classifications will likely create various challenges and opportunities for organizations, their members, and the broader public/society. A set of future research questions is offered for corporate communication researchers and practitioners, who are well positioned to examine this emerging phenomenon.

This paper draws on existing literature related to the growing use of message classifications to offer a list of classification terms and an agenda for future research.

This work describes key classification terms ranging from those found in government (e.g. top secret, confidential) to those in the private sector (e.g. business use only, trademarked) to an even wider set of terms used informally by organizational members (e.g. personal, preliminary). This expanded notion of classification will likely create various challenges and opportunities for organizations, their members, and the broader public/society.

The emerging area of message classification is one of growing relevance to a wide range of organizational communicators as a variety of non-state organizations and their members increasingly use and misuse various terms to restrict their communication. A set of future research questions is offered for corporate communication researchers and practitioners, who are well positioned to examine this emerging phenomenon.

The purpose of this paper is to explore how the use of social media by citizens has impacted the traditional conceptualization and operationalization of political participation in the society.

This study is based on Teorell et al.’s (2007) classification of political participation which is modified to suit the current context of social media. The authors classified 15,460 tweets along three parameters suggested in the framework with help of supervised text classification algorithms.

The analysis reveals that Activism is the most prominent form of political participation undertaken by people on Twitter. Other activities that were undertaken include Formal Political participation and Consumer participation. The analysis also reveals that identity of participant does not play a classifying role as expected from the theoretical framework. It was found that the social media as a platform facilitates new forms of participation which are not feasible offline.

The current work considers only the microblogging platform of Twitter as the data source. For a more comprehensive insight, analysis of other social media platforms is also required.

To the best of the authors’ knowledge, this is one of the few analyses where such a large database covering multiple social media events has been created and analysed using supervised text classification algorithms. A large proportion of previous studies on social media have been based on case study and have limited analysis to only a particular event on social media. Although there exist a few works that have studied a vast and varied collection of social media data (Gaby and Caren, 2012; Shirazi, 2013; Rane and Salem, 2012), such efforts are few in number. This study aims to add to that stream of work where a wider and more generalized set of social media data is studied.

This study aims to analyze communication from the perspective of social marketing, positive emotions, and the topics chosen by Spanish tourist destinations to show their destination image. Additionally, this research shows a message classification model, based on the aforementioned characteristics, that has generated a greater impact, offering clarity to tourism managers on the type of content they should publish to achieve greater visibility.

The methodology used in this work combines content analysis and data mining techniques. The classification tree using the chi-square automatic interaction detector (CHAID) algorithm was selected to determine predictors of like behaviour.

The results show that the predictor variables have been emotions, social marketing and topics. Also, the characteristics of the messages most likely to have a high impact are those related to emotions of joy or happiness, their purpose is behavioural, and they talk about rural, cultural issues, special dates, getaways, or highlights of a town or city for something specific.

This study is the first to analyze the content of the tweets shared by destination tourism managers from a social marketing, positive emotions, and sustainability perspective, determining the possible predictors of likes on Twitter. The authors contribute to the literature by deepening the understanding of how social marketing and the positive emotions promoted drive a more significant impact in tourism communication campaigns on social media. The authors provide destination managers with a way better to understand the variables relevant to users in tourism content.