Partisan Asymmetries in Online Political Activity

We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.Comment: 17 pages, 10 figures, 6 table

Scalable Privacy-Compliant Virality Prediction on Twitter

The digital town hall of Twitter becomes a preferred medium of communication for individuals and organizations across the globe. Some of them reach audiences of millions, while others struggle to get noticed. Given the impact of social media, the question remains more relevant than ever: how to model the dynamics of attention in Twitter. Researchers around the world turn to machine learning to predict the most influential tweets and authors, navigating the volume, velocity, and variety of social big data, with many compromises. In this paper, we revisit content popularity prediction on Twitter. We argue that strict alignment of data acquisition, storage and analysis algorithms is necessary to avoid the common trade-offs between scalability, accuracy and privacy compliance. We propose a new framework for the rapid acquisition of large-scale datasets, high accuracy supervisory signal and multilanguage sentiment prediction while respecting every privacy request applicable. We then apply a novel gradient boosting framework to achieve state-of-the-art results in virality ranking, already before including tweet's visual or propagation features. Our Gradient Boosted Regression Tree is the first to offer explainable, strong ranking performance on benchmark datasets. Since the analysis focused on features available early, the model is immediately applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective Content Analysi

Online Dispute Resolution Through the Lens of Bargaining and Negotiation Theory: Toward an Integrated Model

[Excerpt] In this article we apply negotiation and bargaining theory to the analysis of online dispute resolution. Our principal objective is to develop testable hypotheses based on negotiation theory that can be used in ODR research. We have not conducted the research necessary to test the hypotheses we develop; however, in a later section of the article we suggest a possible methodology for doing so. There is a vast literature on negotiation and bargaining theory. For the purposes of this article, we realized at the outset that we could only use a small part of that literature in developing a model that might be suitable for empirical testing. We decided to use the behavioral theory of negotiation developed by Richard Walton and Robert McKersie, which was initially formulated in the 1960s. This theory has stood the test of time. Initially developed to explain union-management negotiations, it has proven useful in analyzing a wide variety of disputes and conflict situations. In constructing their theory, Walton and McKersie built on the contributions and work of many previous bargaining theorists including economists, sociologists, game theorists, and industrial relations scholars. In this article, we have incorporated a consideration of the foundations on which their theory was based. In the concluding section of the article we discuss briefly how other negotiation and bargaining theories might be applied to the analysis of ODR

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Advances in Teaching & Learning Day Abstracts 2005

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2005

Politeness and Alignment in Dialogues with a Virtual Guide

Language alignment is something that happens automatically in dialogues between human speakers. The ability to align is expected to increase the believability of virtual dialogue agents. In this paper we extend the notion of alignment to affective language use, describing a model for dynamically adapting the linguistic style of a virtual agent to the level of politeness and formality detected in the user’s utterances. The model has been implemented in the Virtual Guide, an embodied conversational agent giving directions in a virtual environment. Evaluation shows that our formality model needs improvement, but that the politeness tactics used by the Guide are mostly interpreted as intended, and that the alignment to the user’s language is noticeable

Adoption of Social Media Search Systems: An IS Success Model Perspective

The social media search system aims at providing an organized and integrated access and search support to a massive amount of unstructured, multilingual, user-generated content in an effective and efficient manner. Previous research on social media analytics mainly focuses on developing and applying advanced analysis methods and/or tools to make sense of the large amount of user-generated data over the Internet. Relatively little effort has been put to specifically examine the social media search system. In this study, we utilize and apply the DeLone and McLean IS Success Model to examine this type of systems. To do it, a lab experiment was conducted, and the results showed that all causal relationships, except for satisfaction to social benefit, specified in the DeLone and McLean IS Success Model hold in the context of the large-scale, social media search system. Specifically, we found that information quality and system quality associated with the system could significantly influence both users’ intention to use and satisfaction toward it, both of which, in turn, had significant impacts on users’ perceived individual benefit and social benefit. In addition, satisfaction could significantly influence intention to use the system. Available at: https://aisel.aisnet.org/pajais/vol10/iss2/4