Social media and sentiment in bioenergy consultation

Purpose: The push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects. Design/methodology/approach: This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’ Findings: Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications: Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Originality/value: Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity

A conceptual framework for studying collective reactions to events in location-based social media

Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events

Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.Comment: ACM SIGMOD Conference 2016, 18 pages, 29 figure

Modeling User Attitude toward Controversial Topics in Online Social Media

The increasing use of social media platforms like Twitter has attracted a large number of online users to express their attitude toward certain topics. Sentiment, opinion, and action, as three essential aspects of user attitude, have been studied separately in various existing research work. Investigating them together not only brings unique challenges but can also help better understand a user's online behavior and benefit a set of applications related to online campaign and recommender systems. In this paper, we present a computational model that estimates individual social media user's attitude toward controversial topics in terms of the three aspects and their relationships. Our model can simultaneously capture the three aspects so as to predict action and sentiment based on one's opinions.Experiments on multiple social media campaign datasets demonstrated that our attitude model can more effectively predict people's sentiment, opinion and action than approaches that treat these aspects separately

Personalized Expert Recommendation: Models and Algorithms

Many large-scale information sharing systems including social media systems, questionanswering sites and rating and reviewing applications have been growing rapidly, allowing millions of human participants to generate and consume information on an unprecedented scale. To manage the sheer growth of information generation, there comes the need to enable personalization of information resources for users — to surface high-quality content and feeds, to provide personally relevant suggestions, and so on. A fundamental task in creating and supporting user-centered personalization systems is to build rich user profile to aid recommendation for better user experience. Therefore, in this dissertation research, we propose models and algorithms to facilitate the creation of new crowd-powered personalized information sharing systems. Specifically, we first give a principled framework to enable personalization of resources so that information seekers can be matched with customized knowledgeable users based on their previous historical actions and contextual information; We then focus on creating rich user models that allows accurate and comprehensive modeling of user profiles for long tail users, including discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile. In particular, this dissertation research makes two unique contributions: First, we introduce the problem of personalized expert recommendation and propose the first principled framework for addressing this problem. To overcome the sparsity issue, we investigate the use of user’s contextual information that can be exploited to build robust models of personal expertise, study how spatial preference for personally-valuable expertise varies across regions, across topics and based on different underlying social communities, and integrate these different forms of preferences into a matrix factorization-based personalized expert recommender. Second, to support the personalized recommendation on experts, we focus on modeling and inferring user profiles in online information sharing systems. In order to tap the knowledge of most majority of users, we provide frameworks and algorithms to accurately and comprehensively create user models by discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile, with each described shortly as follows: —We develop a probabilistic model called Bayesian Contextual Poisson Factorization to discover what users are known for by others. Our model considers as input a small fraction of users whose known-for profiles are already known and the vast majority of users for whom we have little (or no) information, learns the implicit relationships between user?s known-for profiles and their contextual signals, and finally predict known-for profiles for those majority of users. —We explore user’s topic-sensitive opinion bias, propose a lightweight semi-supervised system called “BiasWatch” to semi-automatically infer the opinion bias of long-tail users, and demonstrate how user’s opinion bias can be exploited to recommend other users with similar opinion in social networks. — We study how a user’s topical profile varies geo-spatially and how we can model a user’s geo-spatial known-for profile as the last step in our dissertation for creation of rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating user contexts into the two-layered hierarchical user model for better representation of user’s geo-topic preference by others

Personalized Expert Recommendation: Models and Algorithms

Many large-scale information sharing systems including social media systems, questionanswering sites and rating and reviewing applications have been growing rapidly, allowing millions of human participants to generate and consume information on an unprecedented scale. To manage the sheer growth of information generation, there comes the need to enable personalization of information resources for users — to surface high-quality content and feeds, to provide personally relevant suggestions, and so on. A fundamental task in creating and supporting user-centered personalization systems is to build rich user profile to aid recommendation for better user experience. Therefore, in this dissertation research, we propose models and algorithms to facilitate the creation of new crowd-powered personalized information sharing systems. Specifically, we first give a principled framework to enable personalization of resources so that information seekers can be matched with customized knowledgeable users based on their previous historical actions and contextual information; We then focus on creating rich user models that allows accurate and comprehensive modeling of user profiles for long tail users, including discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile. In particular, this dissertation research makes two unique contributions: First, we introduce the problem of personalized expert recommendation and propose the first principled framework for addressing this problem. To overcome the sparsity issue, we investigate the use of user’s contextual information that can be exploited to build robust models of personal expertise, study how spatial preference for personally-valuable expertise varies across regions, across topics and based on different underlying social communities, and integrate these different forms of preferences into a matrix factorization-based personalized expert recommender. Second, to support the personalized recommendation on experts, we focus on modeling and inferring user profiles in online information sharing systems. In order to tap the knowledge of most majority of users, we provide frameworks and algorithms to accurately and comprehensively create user models by discovering user’s known-for profile, user’s opinion bias and user’s geo-topic profile, with each described shortly as follows: —We develop a probabilistic model called Bayesian Contextual Poisson Factorization to discover what users are known for by others. Our model considers as input a small fraction of users whose known-for profiles are already known and the vast majority of users for whom we have little (or no) information, learns the implicit relationships between user?s known-for profiles and their contextual signals, and finally predict known-for profiles for those majority of users. —We explore user’s topic-sensitive opinion bias, propose a lightweight semi-supervised system called “BiasWatch” to semi-automatically infer the opinion bias of long-tail users, and demonstrate how user’s opinion bias can be exploited to recommend other users with similar opinion in social networks. — We study how a user’s topical profile varies geo-spatially and how we can model a user’s geo-spatial known-for profile as the last step in our dissertation for creation of rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating user contexts into the two-layered hierarchical user model for better representation of user’s geo-topic preference by others

The laws of "LOL": Computational approaches to sociolinguistic variation in online discussions

When speaking or writing, a person often chooses one form of language over another based on social constraints, including expectations in a conversation, participation in a global change, or expression of underlying attitudes. Sociolinguistic variation (e.g. choosing "going" versus "goin'") can reveal consistent social differences such as dialects and consistent social motivations such as audience design. While traditional sociolinguistics studies variation in spoken communication, computational sociolinguistics investigates written communication on social media. The structured nature of online discussions and the diversity of language patterns allow computational sociolinguists to test highly specific hypotheses about communication, such different configurations of listener "audience." Studying communication choices in online discussions sheds light on long-standing sociolinguistic questions that are hard to tackle, and helps social media platforms anticipate their members' complicated patterns of participation in conversations. To that end, this thesis explores open questions in sociolinguistic research by quantifying language variation patterns in online discussions. I leverage the "birds-eye" view of social media to focus on three major questions in sociolinguistics research relating to authors' participation in online discussions. First, I test the role of conversation expectations in the context of content bans and crisis events, and I show that authors vary their language to adjust to audience expectations in line with community standards and shared knowledge. Next, I investigate language change in online discussions and show that language structure, more than social context, explains word adoption. Lastly, I investigate the expression of social attitudes among multilingual speakers, and I find that such attitudes can explain language choice when the attitudes have a clear social meaning based on the discussion context. This thesis demonstrates the rich opportunities that social media provides for addressing sociolinguistic questions and provides insight into how people adapt to the communication affordances in online platforms.Ph.D

Advancing Fine-Grained Emotion Recognition in Short Text

Advanced emotion recognition in text is essential for developing intelligent affective applications, which can recognize, react upon, and analyze users' emotions. Our particular motivation for solving this problem lies in large-scale analysis of social media data, such as those generated by Twitter users. Summarizing users' emotions can enable better understandings of their reactions, interests, and motivations. We thus narrow the problem to emotion recognition in short text, particularly tweets. Another driving factor of our work is to enable discovering emotional experiences at a detailed, fine-grained level. While many researchers focus on recognizing a small number of basic emotion categories, humans experience a larger variety of distinct emotions. We aim to recognize as many as 20 emotion categories from the Geneva Emotion Wheel. Our goal is to study how to build such fine-grained emotion recognition systems. We start by surveying prior approaches to building emotion classifiers. The main body of this thesis studies two of them in detail: crowdsourcing and distant supervision. Based on them, we design fine-grained domain-specific systems to recognize users' reactions to sporting events captured on Twitter and address multiple challenges that arise in that process. Crowdsourcing allows extracting affective commonsense knowledge by asking hundreds of workers for manual annotation. The challenge is in collecting informative and truthful annotations. To address it, we design a human computation task that elicits both emotion category labels and emotion indicators (i.e. words or phrases indicative of labeled emotions). We also develop a methodology to build an emotion lexicon using such data. Our experiments show that the proposed crowdsourcing method can successfully generate a domain-specific emotion lexicon. Additionally, we suggest how to teach and motivate non-expert annotators. We show that including a tutorial and using carefully formulated reward descriptions can effectively improve annotation quality. Distant supervision consists of building emotion classifiers from data that are automatically labeled using some heuristics. This thesis studies heuristics that apply emotion lexicons of limited quality, for example due to missing or erroneous term-emotion associations. We show the viability of such an approach to obtain domain-specific classifiers having substantially better quality of recognition than the initial lexicon-based ones. Our experiments reveal that treating the emotion imbalance in training data and incorporating pseudo-neutral documents is crucial for such improvement. This method can be applied to building emotion classifiers across different domains using limited input resources and thus requiring minimal effort. Another challenge for lexicon-based emotion recognition is to reduce the error introduced by linguistic modifiers such as negation and modality. We design a data analysis method that allows modeling the specific effects of the studied modifiers, both in terms of shifting emotion categories and changing confidence in emotion presence. We show that the effects of modifiers vary across the emotion categories, which indicates the importance of treating such effects at a more fine-grained level to improve classification quality. Finally, the thesis concludes with our recommendations on how to address the examined general challenges of building a fine-grained textual emotion recognition system