Emojis are used frequently in social media. A widely assumed view is that emojis express the emotional state of the user, which has led to research focusing on the expressiveness of emojis independent from the linguistic context. We argue that emojis and the linguistic texts can modify the meaning of each other. The overall communicated meaning is not a simple sum of the two channels. In order to study the meaning interplay, we need data indicating the overall sentiment of the entire message as well as the sentiment of the emojis stand-alone. We propose that Facebook Reactions are a good data source for such a purpose. FB reactions (e.g. "Love" and "Angry") indicate the readers' overall sentiment, against which we can investigate the types of emojis used the comments under different reaction profiles. We present a data set of 21,000 FB posts (57 million reactions and 8 million comments) from public media pages across four countries.

This paper examines China's recent initiative on international social media and assesses its effectiveness in counteracting Western dominance in international communication. Analysing data collected from the Twitter platform of three public accounts run by China's state news media CGTN, People's Daily and Xinhua News, it finds that their news agenda about China focuses on the country's top leaders and achievements, while that about other countries is on breaking news. Their China-related tweets receive more positive replies than their non-China-related tweets, but tweets about China's top leader, Xi Jinping, receive fewer positive replies than soft news items. Analysis of Twitter data of the #southchinasea hashtag finds that China's media mainly compete with US sources for influence. China's state media influence the news agenda on the issue by active and persistent tweeting and drawing retweets. However, US sources are more influential as a whole in setting the news agenda and amplifying certain news events. The study finds evidence that forces seemingly unfriendly to both China and the US attempt to skew the news agenda of #southchinasea using manipulated accounts.

Promoted by sensor, big data and mobile computing technologies, the number of Internet of Things (IoT) applications and services is increasing rapidly. The massive amounts of heterogeneous data produced by a large variety of IoT devices require us to rethink its influence on the network. In this paper, we study the characteristics of IoT data traffic in the context of smart city. We generate data traffic according to the characteristics of different IoT applications. We propose a Gamma modulated wavelet method for statistical characterization of both IoT data and the aggregated traffic, aiming at analyzing the influence of IoT data traffic on the access and core network. By using Gamma function to modulate the coefficients of the wavelet, both the long range and short range dependency of the IoT data traffic can be described through fewer parameters. The Gamma modulation also reduces the independency of the coefficients and improves the accuracy of the Wavelet model.

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This article uses social media network analysis (SMNA) to examine whether there was an astroturfing campaign on Twitter in support of the Adani Carmichael coal mine in 2017. It shows that SMNA can be used to visualize and analyze outsider lobbying activity in issue arenas and is capable of identifying networks of fake opinion. This study found that in April 2017, there was a small network of accounts that made a series of suspiciously similar pro‐Adani tweets that could be considered a form of duplicitous lobbying. However, this study concludes that these posts were likely a weak influence on public opinion in Australia and largely ineffectual as a lobbying tactic. Nevertheless, this analysis shows how communitas public interests can be subverted by covert social media campaigns used in support of corporatas goals, as well as the role digital research methods can play in protecting the integrity on public debates by exposing disingenuous actors.