Norm violation in online communities -- A study of Stack Overflow comments

Norms are behavioral expectations in communities. Online communities are also expected to abide by the rules and regulations that are expressed in the code of conduct of a system. Even though community authorities continuously prompt their users to follow the regulations, it is observed that hate speech and abusive language usage are on the rise. In this paper, we quantify and analyze the patterns of violations of normative behaviour among the users of Stack Overflow (SO) - a well-known technical question-answer site for professionals and enthusiast programmers, while posting a comment. Even though the site has been dedicated to technical problem solving and debugging, hate speech as well as posting offensive comments make the community "toxic". By identifying and minimising various patterns of norm violations in different SO communities, the community would become less toxic and thereby the community can engage more effectively in its goal of knowledge sharing. Moreover, through automatic detection of such comments, the authors can be warned by the moderators, so that it is less likely to be repeated, thereby the reputation of the site and community can be improved. Based on the comments extracted from two different data sources on SO, this work first presents a taxonomy of norms that are violated. Second, it demonstrates the sanctions for certain norm violations. Third, it proposes a recommendation system that can be used to warn users that they are about to violate a norm. This can help achieve norm adherence in online communities.Comment: 16 pages, 8 figures, 2 table

Towards offensive language detection and reduction in four Software Engineering communities

Software Engineering (SE) communities such as Stack Overflow have become unwelcoming, particularly through members' use of offensive language. Research has shown that offensive language drives users away from active engagement within these platforms. This work aims to explore this issue more broadly by investigating the nature of offensive language in comments posted by users in four prominent SE platforms - GitHub, Gitter, Slack and Stack Overflow (SO). It proposes an approach to detect and classify offensive language in SE communities by adopting natural language processing and deep learning techniques. Further, a Conflict Reduction System (CRS), which identifies offence and then suggests what changes could be made to minimize offence has been proposed. Beyond showing the prevalence of offensive language in over 1 million comments from four different communities which ranges from 0.07% to 0.43%, our results show promise in successful detection and classification of such language. The CRS system has the potential to drastically reduce manual moderation efforts to detect and reduce offence in SE communities

Improving Information Systems Sustainability by Applying Machine Learning to Detect and Reduce Data Waste

Big data are key building blocks for creating information value. However, information systems are increasingly plagued with useless, waste data that can impede their effective use and threaten sustainability objectives. Using a constructive design science approach, this work first, defines digital data waste. Then, it develops an ensemble artifact comprising two components. The first component comprises 13 machine learning models for detecting data waste. Applying these to 35,576 online reviews in two domains reveals data waste of 1.9% for restaurant reviews compared to 35.8% for app reviews. Machine learning can accurately identify 83% to 99.8% of data waste; deep learning models are particularly promising, with accuracy ranging from 96.4% to 99.8%. The second component comprises a sustainability cost calculator to quantify the social, economic, and environmental benefits of reducing data waste. Eliminating 5948 useless reviews in the sample would result in saving 6.9 person hours, $2.93 in server, middleware and client costs, and 9.52 kg of carbon emissions. Extrapolating these results to reviews on the internet shows substantially greater savings. This work contributes to design knowledge relating to sustainable information systems by highlighting the new class of problem of data waste and by designing approaches for addressing this problem

A Bayesian approach to norm identification

F. Meneguzzi thanks Conselho Nacional de Desenvolvimento Cient´ıfico e Tecnologico (CNPq) through the Universal Grant (Grant ´ref. 482156/2013-9) and PQ fellowship (Grant ref. 306864/2013-4).Publisher PD

Barriers for Social Inclusion in Online Software Engineering Communities -- A Study of Offensive Language Use in Gitter Projects

Social inclusion is a fundamental feature of thriving societies. This paper first investigates barriers for social inclusion in online Software Engineering (SE) communities, by identifying a set of 11 attributes and organising them as a taxonomy. Second, by applying the taxonomy and analysing language used in the comments posted by members in 189 Gitter projects (with > 3 million comments), it presents the evidence for the social exclusion problem. It employs a keyword-based search approach for this purpose. Third, it presents a framework for improving social inclusion in SE communities.Comment: 6 pages, 5 figures, this paper has been accepted to the short paper track of EASE 2023 conference (see https://conf.researchr.org/track/ease-2023/ease-2023-short-papers-and-posters#event-overview

Norm learning in multi-agent societies

In Normative Multi-Agent Systems (NorMAS), researchers have investigated several mechanisms for agents to learn norms. In the context of agents learning norms, the objectives of the paper are three-fold. First, this paper aims at providing an overview of different mechanisms employed by researchers for norm learning. Second, it discusses the contributions of different mechanisms to the three aspects of active learning namely learning by doing, observing and com- municating. Third, it compares two normative architectures which have an emphasis on the learning of norms. It also discusses the features that should be considered in future norm learning architectures

Boletín Oficial de la Provincia de Oviedo: Número 186 - 1923 agosto 24

Norms are standards of behaviour expected of the members of a society. Norms in human societies help in sustaining social order and facilitating cooperation and coordination among agents. Researchers in multi-agent systems investigate how the concept of norms can serve a similar purpose in artificial societies with autonomous agents. This thesis contributes to two aspects of the study of norms in multi-agent systems through the investigation of mechanisms for norm emergence and norm identification. With the advent of digital societies such as Second Life, software agents that reside in these societies are expected to abide by the norms of those societies. Most works on norms in multi-agent systems assume that agents know the norms a priori. Though this is important, norms that are not explicitly specified by the designers of the society may emerge in open agent societies. Thus there is a need for the study of mechanisms for artificial agent societies which can facilitate norm emergence based on interactions between agents. To that end the first part of this thesis describes a role model mechanism for norm emergence. The thesis also describes how norms can emerge in connection with different types of network topologies. A particle-collision model for constructing dynamic network topologies has been applied to model how two societies can be brought together. Using such a model, norm emergence on dynamic network topologies have been studied. With the uptake of virtual environments which are open and dynamic, agents residing in these societies should be endowed with mechanisms that facilitate norm identification. To that end, the second part of the thesis investigates how a software agent comes to know the norms of the society that it is a part of. To achieve this goal, the thesis presents an internal agent architecture for norm identification.The architecture provides a mechanism for an agent to infer norms based on observing local interactions and signals (sanctions). The software agents equipped with this architecture will be capable of finding two types of norms, prohibition norms and obligation norms. The thesis demonstrates how an agent in a society is able to add, modify and remove norms dynamically. The thesis also demonstrates that an agent is able to identify conditional norms. Thus, the contributions of this thesis are to two aspects of the study of norms, norm emergence and norm identification