Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.

Partial Least Squares Structural Equation Modeling

Cultivating Communities of Practice: A Guide to Managing Knowledge

As a new communication paradigm, social media has promoted information dissemination in social networks. Previous research has identified several content-related features as well as user and network characteristics that may drive information diffusion. However, little research has focused on the relationship between emotions and information diffusion in a social media setting. In this paper, we examine whether sentiment occurring in social media content is associated with a user's information sharing behavior. We carry out our research in the context of political communication on Twitter. Based on two data sets of more than 165,000 tweets in total, we find that emotionally charged Twitter messages tend to be retweeted more often and more quickly compared to neutral ones. As a practical implication, companies should pay more attention to the analysis of sentiment related to their brands and products in social media communication as well as in designing advertising content that triggers emotions.

Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior

What is Twitter

Regression Analysis of Count Data: Preface

In recent years, social media are said to have an impact on the public discourse and communication in the society. In particular, social media are increasingly used in political context. More recently, microblogging services (e.g., Twitter) and social network sites (e.g., Facebook) are believed to have the potential for increasing political participation. While Twitter is an ideal platform for users to spread not only information in general but also political opinions publicly through their networks, political institutions (e.g., politicians, political parties, political foundations, etc.) have also begun to use Facebook pages or groups for the purpose of entering into direct dialogs with citizens and encouraging more political discussions. Previous studies have shown that from the perspective of political institutions, there is an emerging need to continuously collect, monitor, analyze, summarize, and visualize politically relevant information from social media. These activities, which are subsumed under “social media analytics,” are considered difficult tasks due to a large numbers of different social media platforms as well as the large amount and complexity of information and data. Systematic tracking and analysis approaches along with appropriate scientific methods and techniques in political domain are still lacking. In this paper, we propose a methodological framework for social media analytics in political context. More specifically, our framework summarizes most important politically relevant issues from the perspective of political institutions and corresponding methodologies from different scientific disciplines.

Social media and political communication: a social media analytics framework

Microblogging services such as Twitter are said to have the potential for increasing political participation. Given the feature of 'retweeting' as a simple yet powerful mechanism for information diffusion, Twitter is an ideal platform for users to spread not only information in general but also political opinions through their networks as Twitter may also be used to publicly agree with, as well as to reinforce, someone's political opinions or thoughts. Besides their content and intended use, Twitter messages ('tweets') also often convey pertinent information about their author's sentiment. In this paper, we seek to examine whether sentiment occurring in politically relevant tweets has an effect on their retweetability (i.e., how often these tweets will be retweeted). Based on a data set of 64,431 political tweets, we find a positive relationship between the quantity of words indicating affective dimensions, including positive and negative emotions associated with certain political parties or politicians, in a tweet and its retweet rate. Furthermore, we investigate how political discussions take place in the Twitter network during periods of political elections with a focus on the most active and most influential users. Finally, we conclude by discussing the implications of our results.

/pdf/political-communication-and-influence-through-microblogging-3bcgqljel4.pdf

Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior

In this contribution, we introduce “social media analytics” (SMA) as an emerging interdisciplinary research field that, in our view, will have a significant impact on social media-related future research from across different academic disciplines. Despite a number of challenges, we argue that SMA can provide other disciplines – including IS – with methodological foundations for research that focuses on social media. Furthermore, we believe that SMA can help IS research to develop decision-making or decision-aiding frameworks by tackling the issue of social media-related performance measurement, which has been challenging until now. Moreover, SMA can provide architectural designs and solution frameworks for new social media-based applications and information systems. Finally, we call for an interdisciplinary SMA research agenda as well as a significantly increased level of interdisciplinary research co-operation, which must aim to generate significant advancements in scientific methods for analyzing social media, as well as to answer research questions from across different disciplines.

/pdf/social-media-analytics-an-interdisciplinary-approach-and-its-wc4mgajapw.pdf

Social Media Analytics: An Interdisciplinary Approach and Its Implications for Information Systems

In this paper, the authors investigate how Twitter is used for political communication during election periods with a specific focus on characteristics and communication behavior of influential accounts. Our analysis focuses on the state parliament election in Berlin (Germany) on 18 September 2011. Tweets of the top-30 most retweeted users are analyzed with respect to content-related features such as emotionality, appraisal of political parties or politicians, and topics. Furthermore, the authors test hypotheses regarding the relationship between sentiment in terms of emotionality and appraisals occurring in tweets and their quantity and speed of dissemination. Our work helps both researchers and politicians to better understand the nature of influentials in political communication and the role of sentiment in information diffusion on Twitter.

Linh Dang-Xuan

Papers

Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior

Social media and political communication: a social media analytics framework

Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior

Social Media Analytics: An Interdisciplinary Approach and Its Implications for Information Systems

An Investigation of Influentials and the Role of Sentiment in Political Communication on Twitter during Election Periods