Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue

Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue}, author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference}, year={2013}

Computational Controversy

Climate change, vaccination, abortion, Trump: Many topics are surrounded by fierce controversies. The nature of such heated debates and their elements have been studied extensively in the social science literature. More recently, various computational approaches to controversy analysis have appeared, using new data sources such as Wikipedia, which help us now better understand these phenomena. However, compared to what social sciences have discovered about such debates, the existing computational approaches mostly focus on just a few of the many important aspects around the concept of controversies. In order to link the two strands, we provide and evaluate here a controversy model that is both, rooted in the findings of the social science literature and at the same time strongly linked to computational methods. We show how this model can lead to computational controversy analytics that have full coverage over all the crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social Informatics (SocInfo) 201

How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook

Detection of Sarcasm and Nastiness: New Resources for Spanish Language

The main goal of this work is to provide the cognitive computing community with valuable resources to analyze and simulate the intentionality and/or emotions embedded in the language employed in social media. Specifically, it is focused on the Spanish language and online dialogues, leading to the creation of SOFOCO (Spanish Online Forums Corpus). It is the first Spanish corpus consisting of dialogic debates extracted from social media and it is annotated by means of crowdsourcing in order to carry out automatic analysis of subjective language forms, like sarcasm or nastiness. Furthermore, the annotators were also asked about the context need when taking a decision. In this way, the users’ intentions and their behavior inside social networks can be better understood and more accurate text analysis is possible. An analysis of the annotation results is carried out and the reliability of the annotations is also explored. Additionally, sarcasm and nastiness detection results (around 0.76 F-Measure in both cases) are also reported. The obtained results show the presented corpus as a valuable resource that might be used in very diverse future work.This study was partially funded by the Spanish Government (TIN2014-54288-C4-4-R and TIN2017-85854-C4-3-R) by the European Unions’s H2020 program under grant 769872 and by the National Science Foundation of USA (NSF CISE R1 #1202668

Определение отношений между пользователями социальной сети Twitter на основе анализа текста сообщений

Данная ВКР посвящена разработке классификатора отношений между пользователем-автором сообщения и пользователями-комментаторами социальной сети Twitter и его программной реализации.This master thesis is dedicated to developing a classifier of relations between the users of the Twitter social network and its software implementation

The significance of social factors in the planning and implementation of feral cat management programs

Feral cat management is the subject of debate in many countries due to conflicting ecological, ethical, economic, and social reasons. Perceptions and attitudes around the various possible feral cat management methods influence socially and politically acceptable management. While most of the recent research conducted on feral cat management has taken technical aspects into account, there is considerably less emphasis on how the social aspects may influence success. This thesis aims to compare global differences in feral cat management approaches, and to improve the understanding of how social factors influence attitudes around different feral cat management methods. The first objective was to investigate global attitudes towards feral cats by analysing international scientific literature around feral cat management with a focus on social perspectives. The literature review (chapter 2) presents global comparisons by providing insight into how feral cats are perceived by stakeholders in various countries, and what social factors influence these perceptions worldwide. The second objective of this thesis focused on analysing public attitudes towards feral cats and their management in both a regional and international context and determined the countries and groups that contributed greatly to the social media narrative around feral cats. In this portion of the study, Twitter data was used to distinguish the language used by differing groups in various countries to portray attitudes towards feral cats, as detailed in chapter 3. The final objective focused on determining the social factors that influence public attitudes and perceptions of methods used in feral cat management, and the social acceptability of these methods. A landholder questionnaire was used to assess the acceptance of several feral cat management methods on properties on Kangaroo Island, South Australia and near to the Grampians National Park region of western Victoria. It was found that gender, land use, previous knowledge of feral cat management methods, and location influenced the likelihood of participants to accept and use various feral cat management methods on their properties, as covered over chapters 4 and 5. This study highlights the importance of communication and information sharing in feral cat management, including knowledge about control tools, and demonstrates that education about feral cat impacts can increase support for management. It further suggests that feral cat management in any locality needs to consider the potential for regional differences that might stem from variations in culture and environment=. Feral cat management in any space requires investigation into the demographic and social factors that influence levels of support for particular interventions in an area, and that includes appealing to the public and engaging with the local community by interacting with them directly and educating while spreading awareness.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 202