Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceeding

Negative emotion under haze: an investigation based on the microblog and weather records of Tianjin, China

Nowadays, many big cities are suffering from heavy air pollution and continuous haze weather. Compared with the threat on physical health, the influence of haze on people’s mental health is much less discussed in the current literature. Emotion is one of the most important indicators of mental health. To understand the negative impact of haze weather on the emotion of the people, we conducted an investigation based on historical weather records and microblog data in Tianjin, China. Specifically, an emotional thesaurus was generated with a microblog corpus collected from sample data. Based on the thesaurus, the public emotion under haze was statistically described. Then, through correlation analysis and comparative study, the relation and seasonal variation of haze and negative emotion of the public were well discussed. According to the study results, there was indeed a correlation between haze and negative emotion of the public, but the strength of this relationship varied under different conditions. The level of air pollution and weather context were both important factors that influence the mental effects of haze, and diverse patterns of negative emotion expression were demonstrated in different seasons of a year. Finally, for the benefit of people’s mental health under haze, recommendations were given for haze control from the side of government

FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT

Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the ability to recognize emotions in a particular kind of text, microblogs, has important applications in sentiment analysis and affective computing. In order to build computational models that can recognize the emotions represented in tweets we need to identify a set of suitable emotion categories. Prior work has mainly focused on building computational models for only a small set of six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). This thesis describes a taxonomy of 28 emotion categories, an expansion of these six basic emotions, developed inductively from data. This set of 28 emotion categories represents a set of fine-grained emotion categories that are representative of the range of emotions expressed in tweets, microblog posts on Twitter. The ability of humans to recognize these fine-grained emotion categories is characterized using inter-annotator reliability measures based on annotations provided by expert and novice annotators. A set of 15,553 human-annotated tweets form a gold standard corpus, EmoTweet-28. For each emotion category, we have extracted a set of linguistic cues (i.e., punctuation marks, emoticons, emojis, abbreviated forms, interjections, lemmas, hashtags and collocations) that can serve as salient indicators for that emotion category. We evaluated the performance of automatic classification techniques on the set of 28 emotion categories through a series of experiments using several classifier and feature combinations. Our results shows that it is feasible to extend machine learning classification to fine-grained emotion detection in tweets (i.e., as many as 28 emotion categories) with results that are comparable to state-of-the-art classifiers that detect six to eight basic emotions in text. Classifiers using features extracted from the linguistic cues associated with each category equal or better the performance of conventional corpus-based and lexicon-based features for fine-grained emotion classification. This thesis makes an important theoretical contribution in the development of a taxonomy of emotion in text. In addition, this research also makes several practical contributions, particularly in the creation of language resources (i.e., corpus and lexicon) and machine learning models for fine-grained emotion detection in text

Analysis of Users’ Sentiments in Social Media (on the Example of the Astrakhan Region)

The article is devoted to the studying of the opinions and sentiments of users of regional communities in the social network VKontakte using methods of machine analysis of text data, supplemented by sociological research methods. In the course of the study, we identified a list of current topics discussed by the inhabitants of the region, determined the most frequently mentioned persons, and analyzed the tone of their mention. Additionally, on the basis of the obtained results, the index of subjective (non-) well-being (ISW) was calculated for each district of the region and a map of the emotional coloring of posts from the communities of the analyzed social network was built. The results of the study can be used to monitor the situation in the region, finding problem areas, elicitation opinion leaders (popular personalities of the region that have a special influence on the opinion of the population), as well as identify the most interesting topics and urgent problems for the population. In perspective, this method of monitoring the social sentiments of the population of the region can be improved by automating the addition of new data to the analytical project. In the future, the addition of mathematical models to the system will make it possible to create graphs for predicting further changes in the region

Environmentally vulnerable or sensitive groups exhibiting varying concerns towards air pollution can drive government response to improve air quality

Air pollution seriously threatens human health, and its consequences are particularly prevalent among environmentally vulnerable or sensitive groups. However, whether the concerns among these groups are different and how they affect air pollution governance remain unclear. Here, we extract 3.8 million haze-related posts from China’s Sina Weibo and analyse the concerns raised by these groups by constructing an air pollution notability index. The results show that protection is the key theme for women aged 20-35 years, while elderly individuals are easily influenced by haze-related product ads yet lack awareness of scientific-based protection. Concerns shared by young individuals are more effective in pressuring the government in cities that experience higher levels of pollution. Concerns shared by women are more effective in cities that experience lower levels of pollution. This study evidences the influence of the public concerns conveyed via social media on air pollution governance in China

The Power of Electronic Channels and Electronic Political Efficacy: Electronic Participation Discourse

Electronic participation (e-participation) has become an increasingly important phenomenon. Drawing from the information system success model and political efficacy, we built a research model that investigates how government feedback quality, information quality, and channel quality associated with an e-participating channel can affect people’s electronic political efficacy, which, in turn, can influence users’ post-adoption attitudes and behaviors. We also explored the relationship between offline political efficacy and electronic political efficacy. Based on data that we collected from a survey, we found that electronic political efficacy distinctly differs from offline political efficacy though the latter influences the former one. Four channel features (i.e., government feedback quality, information quality, media richness, and social presence of citizens) can affect electronic political efficacy, which, in turn, has a positive influence on e-participation continuance intention and positive word of mouth. We also found that government feedback quality negatively moderated the impact that offline political efficacy had on electronic political efficacy. This study provides useful insights for both researchers and practitioners on the power of electronic channels in electronic participation in public discourse

Soundscape in Urban Forests

This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests