Detecting deceptive reviews using Argumentation

Cocarascu, Oana; Toni, Francesca

doi:10.1145/2970030.2970031

Cited by 17 publications

(17 citation statements)

References 36 publications

(55 reference statements)

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“…One possible direction is to introduce argumentation mining [29], which automatically identifies arguments structure and the relation between the arguments, then intertwine bipolar argumentation framework to identify conflictfree features and issues based on their supporting and attacking arguments [37,39,40]. Another research direction is to identify sentiments and polarity of each user comments in the Reddit forum and then apply a bipolar argumentation framework to identify conflict-free arguments, features and issues underneath rationale [41][42][43][44]. Another possible future direction is to use, state-of-the-art deep learning algorithms on a relatively larger data set and improves the performance of the existing automated algorithms.…”

Section: Discussionmentioning

confidence: 99%

Requirements knowledge acquisition from online user forums

2020

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Online discussion forums can be used for reflecting on the overall user experience of a system. If a user forum is well-structured, it can be a valuable source of requirements-related information, which can potentially be accommodated in the requirements engineering process to enhance the current and future software. However, presently, there are limited approaches for extracting such requirements-related information from the relevant community forums. To fill this gap, this study proposes an automated approach, which automatically identifies requirements information using natural language processing and machine learning. For this purpose, the authors analysed 3319 user comments collected from the seven discussion topics in the Reddit forum. Then, using a content analysis approach, they studied how frequently end-users submit such information across each discussion topic. Also, they developed an automated approach that identifies key stakeholders, who frequently contribute his rationales in the forum discussion. Further, they employed different machine learning algorithms to classify user comments into rationale elements of different types. The authors' results show that online forums, such as Reddit, can be a rich source of requirements elicitation. Also, machine learning is a promising tool to detect user's rationale and identify different kinds of requirements modelling elements.

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Section: Discussionmentioning

confidence: 99%

Requirements knowledge acquisition from online user forums

2020

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“…One possible future direction is to use argumentation mining 74 with the proposed CrowdRE-Arg approach, which will help in automatically identifying the argumentative structure in the user comments, such as the conclusion, the premises and the complete arguments, even it helps in identifying the relations between the arguments. 75 Also, we can extend the CrowdRE-Arg approach by identifying the sentiments and polarity of each crowd-user comment in the end-users discussion with the help of natural language processing, and then intertwine the BAP to overcome the conflicting viewpoints and identify winning arguments. 60 To enhance the decision-making process in the CrowdRE-Arg approach, we can introduce Karma_Score in the decision-making process, which will help in ensuring the competency of the crowd-users who are frequently participating in the decision-making process.…”

Section: Discussionmentioning

confidence: 99%

“…It automatically identifies argumentative structure in a textual document, such as the conclusion, premises and complete arguments, even it helps in identifying relations between arguments. 75 Recently, argumentation mining has been applied in social media, 61 Twitter 58 and end-users reviews. 60 Bosc et al 58 proposed an arguments mining approach, inspired from BAP, which first identifies tweets having argumentative structure, grouped those tweets that discuss similar topics and construct an argumentation graph, then predicts using deep learning algorithms that whether such tweets attack or support each other; finally, winning arguments are identified based on bipolar argumentation semantics.…”

Section: Argumentation Miningmentioning

confidence: 99%

“…Also, Cocarascu and Toni 60 proposed a framework to mine topic‐dependent BAP by exploring hotel reviews to identify the supporting and attacking arguments about similar topics and then take advantage of built‐in semantics of BAP to find the winning arguments. Further, Cocarascu and Toni 76 uses AF together with machine learning algorithms to identify deceptive reviews. The proposed approach is different than the above‐mentioned approaches, as the authors are interested in automatically identify requirements‐related information from user comments in the forum based on their supporting and attacking arguments.…”

Section: Related Workmentioning

confidence: 99%

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Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE‐Arg framework

Khan

Liu

Wen

et al. 2020

J Software Evolu Process

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Due to the pervasive use of online forums and social media, users' feedback are more accessible today and can be used within a requirements engineering context. However, such information is often fragmented, with multiple perspectives from multiple parties involved during ongoing interactions. In this paper, the authors propose a Crowdbased Requirements Engineering approach by Argumentation (CrowdRE-Arg). The framework is based on the analysis of the textual conversations found in user forums, identification of features, issues and the arguments that are in favour or opposing a given requirements statement. The analysis is to generate an argumentation model of the involved user statements, retrieve the conflicting-viewpoints, reason about the winning-arguments and present that to systems analysts to make informedrequirements decisions. For this purpose, the authors adopted a bipolar argumentation framework and a coalition-based meta-argumentation framework as well as user voting techniques. The CrowdRE-Arg approach and its algorithms are illustrated through two sample conversations threads taken from the Reddit forum. Additionally, the authors devised algorithms that can identify conflict-free features or issues based on their supporting and attacking arguments. The authors tested these machine learning algorithms on a set of 3,051 user comments, preprocessed using the content analysis technique. The results show that the proposed algorithms correctly and efficiently identify conflict-free features and issues along with their winning arguments. K E Y W O R D S argumentation, machine learning, natural language processing, new features, requirements, user forum 1 | INTRODUCTION Recently, with the increasing popularity and development of social media, the role of rationale management became more prominent for marketdriven software applications. Mainly, rationale management focuses on capturing and recording the justification why a decision was taken. 1 In software engineering literature, requirements and design rationale management have been a main research area. 2,3 Software developers of social applications make various decisions in finalizing the set of software features. 4 To justify their decisions, different alternatives are considered, and argumentation that helps in finalizing the decision constitutes the software rationale. 5 During software evolution, the rationale serves as a source for development decision making and enhancing the software system explainability. 6,7 Also, the rationale is meant to provide a sound reasoning of the chosen design of the system 8 and help in the understanding of

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“…In argumentation-based aggregation of user reviews (e.g. see [8]) in a context such as Trip Advisor, where the base score may correspond to the number of users voting for an argument, this affords a guarantee to users that any attacker or supporter they add will have an effect on a targeted argument's evaluation unless their argument is rejected, for example if it is flagged for a particular reason. This behaviour seems to be ideal for a setting such as this in which malicious arguments may be present (and flagged), but it is nonetheless important for non-flagged arguments to have an effect to entice users to participate.…”

Section: Novel Instances Of Gp2mentioning

confidence: 99%

On Instantiating Generalised Properties of Gradual Argumentation Frameworks

Rago

Baroni

Toni

2018

Lecture Notes in Computer Science

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Several gradual semantics for abstract and bipolar argumentation have been proposed in the literature, ascribing to each argument a value taken from a scale, i.e. an ordered set. These values represent the arguments' dialectical status and provide an indication of their dialectical strength, in the context of the given argumentation framework. These research efforts have been complemented by formulations of several properties that these gradual semantics may satisfy. More recently a synthesis of many literature properties into more general groupings based on parametric definitions has been proposed. In this paper we show how this generalised parametric formulation enables the identification of new properties not previously considered in the literature and discuss their usefulness to capture alternative requirements coming from different application contexts.

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Detecting deceptive reviews using Argumentation

Cited by 17 publications

References 36 publications

Requirements knowledge acquisition from online user forums

Requirements knowledge acquisition from online user forums

Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE‐Arg framework

On Instantiating Generalised Properties of Gradual Argumentation Frameworks

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