Concerned or Apathetic? Using Social Media Platform (Twitter) to Gauge the Public Awareness about Wildlife Conservation: A Case Study of the Illegal Rhino Trade.

The illegal wildlife trade is resulting in worldwide biodiversity loss and species' extinction. It should be exposed so that the problems of conservation caused by it can be highlighted and resolutions can be found. Social media is an effective method of information dissemination, providing a real-time, low-cost, and convenient platform for the public to release opinions on wildlife protection. This paper aims to explore the usage of social media in understanding public opinions toward conservation events, and illegal rhino trade is an example. This paper provides a framework for analyzing rhino protection issues by using Twitter. A total of 83,479 useful tweets and 33,336 pieces of users' information were finally restored in our database after filtering out irrelevant tweets. With 2422 records of trade cases, this study builds up a rhino trade network based on social media data. The research shows important findings: (1) Tweeting behaviors are somewhat affected by the information of traditional mass media. (2) In general, countries and regions with strong negative sentiment tend to have high volume of rhino trade cases, but not all. (3) Social celebrities' participation in activities arouses wide public concern, but the influence does not last for more than a month. NGOs, GOs, media, and individual enterprises are dominant in the dissemination of information about rhino trade. This study contributes in the following ways: First, this paper conducts research on public opinions toward wildlife conservation using natural language processing technique. Second, this paper offers advice to governments and conservationist organizations, helping them utilize social media for protecting wildlife.

Effects of PM2.5 on People's Emotion: A Case Study of Weibo (Chinese Twitter) in Beijing.

PM2.5 not only harms physical health but also has negative impacts on the public's wellbeing and cognitive and behavioral patterns. However, traditional air quality assessments may fail to provide comprehensive, real-time monitoring of air quality because of the sparse distribution of air quality monitoring stations. Overcoming some key limitations of traditional surface monitoring data, Web-based social media platforms, such as Twitter, Weibo, and Facebook, provide a promising tool and novel perspective for environmental monitoring, prediction, and evaluation. This study aims to investigate the relationship between PM2.5 levels and people's emotional intensity by observing social media postings. This study defines the "emotional intensity" indicator, which is measured by the number of negative posts on Weibo, based on Weibo data related to haze from 2016 and 2017. This study estimates sentiment polarity using a recurrent neural networks model based on LSTM (Long Short-Term Memory) and verifies the correlation between high PM2.5 levels and negative posts on Weibo using a Pearson correlation coefficient and multiple linear regression model. This study makes the following observations: (1) Taking the two-year data as an example, this study recorded the significant influence of PM2.5 levels on netizens' posting behavior. (2) Air quality, meteorological factors, the seasons, and other factors have a strong influence on netizens' emotional intensity. (3) From a quantitative viewpoint, the level of PM2.5 varies by 1 unit, and the number of negative Weibo posts fluctuates by 1.0168 units. Thus, it can be concluded that netizens' emotional intensity is significantly positively affected by levels of PM2.5. The high correlation between PM2.5 levels and emotional intensity and the sensitivity of social media data shows that social media data can be used to provide a new perspective on the assessment of air quality.