Analysing engagement towards the 2014 Earth Hour Campaign in Twitter

Earth Hour (EH) is a large-scale campaign launched by the World Wide Fund For Nature (WWF) every year to raise awareness about environmental issues. Although the EH campaign is active on social media, there is currently no systematic way of assessing its impact on public engagement and the topics they post about. In this paper we study engagement towards the 2014 EH campaign on Twitter. By analysing more than 35K tweets around the campaign we observed that longer posts, easier to read and with positive sentiment generated higher attention levels. Conversations were driven by the main themes of the campaign (super hero, the panda, etc.), but engagement towards these themes did not always translate in engagement towards environmental issues. Users decreased their engagement towards the topics of the campaign after it finished, but these topics still remained in their conversations one month later

Challenges of Evaluating Sentiment Analysis Tools on Social Media

This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems. Firstly, these are due to differences in corpus annotation guidelines and sentiment class distribution. Secondly, different systems often make different assumptions about how to interpret certain statements, e.g. tweets with URLs. In order to study the impact of these on evaluation results, this paper focuses on tweet sentiment analysis in particular. One existing and two newly created corpora are used, and the performance of four different sentiment analysis systems is reported; we make our annotated datasets and sentiment analysis applications publicly available. We see considerable variations in results across the different corpora, which calls into question the validity of many existing annotated datasets and evaluations, and we make some observations about both the systems and the datasets as a result

Strategi Global Civil Society di Level Lokal: Kasus Earth Hour Malang

This article examines Earth Hour Malang as a strategy of global civil society at the local level in campaigning green living and encouraging public awareness for the environment. By using global civil society conceptualization, visibility, and audibility strategies, primary data was collected through interviews with Earth Hour Malang activists as well as documentation studies on various Earth Hour Malang’s social media, also supported with secondary data. The results indicates that Earth Hour Malang carries out an active, consistent and continuous visibility and audibility strategy by taking various direct actions (offline) and online through various instruments, such as social media, radio, video, and television. This article contributes to studies related to the existence of global civil society at the local level

Understanding Health Video Engagement: An Interpretable Deep Learning Approach

Health misinformation on social media devastates physical and mental health, invalidates health gains, and potentially costs lives. Understanding how health misinformation is transmitted is an urgent goal for researchers, social media platforms, health sectors, and policymakers to mitigate those ramifications. Deep learning methods have been deployed to predict the spread of misinformation. While achieving the state-of-the-art predictive performance, deep learning methods lack the interpretability due to their blackbox nature. To remedy this gap, this study proposes a novel interpretable deep learning approach, Generative Adversarial Network based Piecewise Wide and Attention Deep Learning (GAN-PiWAD), to predict health misinformation transmission in social media. Improving upon state-of-the-art interpretable methods, GAN-PiWAD captures the interactions among multi-modal data, offers unbiased estimation of the total effect of each feature, and models the dynamic total effect of each feature when its value varies. We select features according to social exchange theory and evaluate GAN-PiWAD on 4,445 misinformation videos. The proposed approach outperformed strong benchmarks. Interpretation of GAN-PiWAD indicates video description, negative video content, and channel credibility are key features that drive viral transmission of misinformation. This study contributes to IS with a novel interpretable deep learning method that is generalizable to understand other human decision factors. Our findings provide direct implications for social media platforms and policymakers to design proactive interventions to identify misinformation, control transmissions, and manage infodemics.Comment: WITS 2021 Best Paper Awar