User generated content in the social media platforms are being considered as an important source for information about consumers and other emerging trends by the businesses. Using Twitter analytics, the paper presents insights on trends and discussions about the Internet of Things (IoT). Using relevant hashtags, 40,387 tweets were collected in early 2016. The analysis had followed three major approaches: descriptive analysis, content analysis and network analysis. The tools R and NodeXL were used for the analysis. The findings showed major themes like business concerns, scope of applications, security, emerging smart technologies and manufacturing. The sentiments of emotions and polarity differed across these themes. The top individual and industrial influencers were identified. The analysis also detected the highly-associated words and hashtags, and different user communities and how they are connected. Business implications of the findings and limitations are also elaborated.

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Social Media platforms play a major role in spreading information. Twitter, is one such platform which is used by millions of people to share information every day. Twitter with the recent introduction of a feature that helps its users to attach images to a tweet has changed the dynamics of tweeting. Many people now prefer to tweet with images. This study tries to analyse and predict the popularity of such tweets. This study uses learning mechanisms like decision tree, neural networks and random forests to learn the tweets posted by people with a higher number of followers. Image parameters, network variables, transactional, and historical variables of a tweet are identified and are trained for predicting the test data. This study can help businesses to build better social media tools, which allows customers to tweet data at the right time. This study also identifies the contribution of various parameters that may help a tweet to go viral.

The Government of India through its various programmes like Digital India aims to provide digital literacy to all its citizens. Towards this mission, National Digital Literacy Mission and Digital Saksharata Abhiyan conducted various training programmes throughout the country. The authors collected details of 5 lakh participants, who successfully completed the training programme and to learn the quality of the training and identify its impact 30,003 participants were interviewed based on a systematic sampling. This study based on the survey and interview identifies a model to predict and validate the impact of similar Digital Literacy training programmes in India. The various components identified in this study were then ranked based on the participant responses using the analytical hierarchy process. The various constructs identified for assessing the quality of training includes conduct, delivery and content, and perceived value. The impact of training was measured using knowledge gained, comfort level achieved and frequency of usage. To identify the impact of training a simple linear regression technique was also used.

Purpose Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close communities (represented by cliques), the size of these close communities and its impact on information virality. Design/methodology/approach This study identified 6,786 users from over 11 million tweets for analysis using sentiment mining and network science methods. Inferential analysis has also been established by introducing multiple regression analysis and path analysis. Findings Sentiments of content did not have a significant impact on the information virality. However, there exists a stagewise development relationship between communities of close friends, user reputation and information propagation through virality. Research limitations/implications This paper contributes to the theory by introducing a stagewise progression model for influencers to manage and develop their social networks. Originality/value There is a gap in the existing literature on the role of the number and size of cliques on information propagation and virality. This study attempts to address this gap.