Using discussion logic in analyzing online group discussions: A text mining approach

2019 | Information & Management | Citations: 1

Authors: Deng, Shasha; Zhou, Yilu; Zhang, Pengzhu; Abbasi, Ahmed

Abstract: We propose a Discussion Logic-based Text Analytics (DiLTA) framework, which comb ... Expand

Abstract: We propose a Discussion Logic-based Text Analytics (DiLTA) framework, which combines theories developed in social science and text mining fields. The framework extracts features that uncover discussion logic and uses these features in analyzing online discussions. A series of models are proposed including conversation disentanglement, coherence analysis, and visualization. Validation experiments showed that DiLTA achieved significantly superior performance over existing text analytics methods in reconstructing internal structure. A case study using DiLTA-enabled visualization on a healthcare forum illustrates the great potential of DiLTA in assisting comprehension of the internal linkage, structure, and logic of online group discussions. Collapse

Topics: social network group support system chat room social media web forum

Methods: machine learning computational algorithm experiment coherence analysis longitudinal research

Theories: argumentation theory

Text Analytics to Support Sense-Making in Social Media: A Language-Action Perspective

2018 | Management Information Systems Quarterly | Citations: 0

Authors: Abbasi, Ahmed; Zhou, Yilu; Deng, Shasha; Zhang, Pengzhu

Abstract: Social media and online communities provide organizations with new opportunities ... Expand

Abstract: Social media and online communities provide organizations with new opportunities to support their businessrelated functions. Despite their various benefits, social media technologies present two important challenges for sense-making. First, online discourse is plagued by incoherent, intertwined conversations that are often difficult to comprehend. Moreover, organizations are increasingly interested in understanding social media participants’ actions and intentions; however, existing text analytics tools mostly focus on the semantic dimension of language. The language-action perspective (LAP) emphasizes pragmatics; not what people say but, rather, what they do with language. Adopting the design science paradigm, we propose a LAP-based text analytics framework to support sense-making in online discourse. The proposed framework is specifically intended to address the two aforementioned challenges associated with sense-making in online discourse: the need for greater coherence and better understanding of actions. We rigorously evaluate a system that is developed based on the framework in a series of experiments using a test bed encompassing social media data from multiple channels and industries. The results demonstrate the utility of each individual component of the system, and its underlying framework, in comparison with existing benchmark methods. Furthermore, the results of a user experiment involving hundreds of practitioners, and a four-month field experiment in a large organization, underscore the enhanced sense-making capabilities afforded by text analytics grounded in LAP principles. The results have important implications for online sense-making and social media analytics. Collapse

Topics: sensemaking social media social network web forum productivity

Methods: experiment longitudinal research coherence analysis machine learning field experiment

Theories: speech act theory language action perspective statistical learning theory

Social structural behavior of deception in computer-mediated communication

2014 | Decision Support Systems | Citations: 0

Authors: Pak, Jinie; Zhou, Lina

Abstract: Deception essentially takes place in social interaction. While deception has bee ... Expand

Abstract: Deception essentially takes place in social interaction. While deception has been studied from the perspective of interpersonal interaction, little is known about social structural characteristics of deceptive communication. To fill the knowledge gap, this research investigates deception behavior in computer mediated communication (CMC) via the lens of social structure by answering the questions of how one deceiver socially interacts with multiple receivers and what structural characteristics can be used to delineate deception in CMC. To this end, we first conceptualize deception in terms of social structure by drawing on the interpersonal deception and social network theories. We then propose a model of structural behaviors of deception in CMC that consists of three components: centrality, cohesion, and similarity, followed by an empirical evaluation of the model with real-world data collected from a game website. The findings of this study provide new evidence that deception is a strategic activity where the deceiver juggles between the dual goals of promoting his or her deceptive agenda and avoiding detection. Collapse

Topics: social network website deception detection terrorist incident viral marketing

Methods: longitudinal research coherence analysis descriptive statistic statistical hypothesis test cluster analysis

Theories: social network theory interpersonal deception theory

Turning Unstructured and Incoherent Group Discussion into DATree: A TBL Coherence Analysis Approach

2011 | International Conference on Information Systems | Citations: 1

Authors: Deng, Shasha; Zhang, Pengzhu; Zhou, Yilu

Abstract: Despite the rapid growth of user-generated unstructured text from online group d ... Expand

Abstract: Despite the rapid growth of user-generated unstructured text from online group discussions, business decision-makers are facing the challenge of understanding its highly incoherent content. Coherence analysis attempts to reconstruct the order of discussion messages. However, existing methods only focus on system and cohesion features. While they work with asynchronous discussions, they fail with synchronous discussions because these features rarely appear. We believe that discussion logic features play an important role in coherence analysis. Therefore, we propose a TCA method for coherence analysis, which is composed of a novel message similarity measure algorithm, a subtopic segmentation algorithm and a TBL-based classification algorithm. System, cohesion and discussion logic features are all incorporated into our TCA method. Results from experiments showed that the TCA method achieved significantly better performance than existing methods. Furthermore, we illustrate that the DATree generated by the TCA method can enhance decision-makers’ content analysis capability. Collapse

Topics: group support system systems design evaluation criteria chat room blog

Methods: group interview coherence analysis longitudinal research machine learning computational algorithm

Theories: argumentation theory

Beyond keyword and cue-phrase matching: A sentence-based abstraction technique for information extraction

2006 | Decision Support Systems | Citations: 0

Authors: Chan, Samuel W. K.

Abstract: With the explosion in the quantity of on-line text and multimedia information in ... Expand

Abstract: With the explosion in the quantity of on-line text and multimedia information in recent years, there has been a renewed interest in the automated extraction of knowledge and information in various disciplines. In this paper, we provide a novel quantitative model for the creation of a summary by extracting a set of sentences that represent the most salient content of a text. The model is based on a shallow linguistic extraction technique. What distinguishes it from previous research is that it does not work on the detection of specific keywords or cue-phrases to evaluate the relevance of the sentence concerned. Instead, the attention is focused on the identification of the main factors in the textual continuity. Simulation experiments suggest that this technique is useful because it moves away from a purely keyword-based method of textual information extraction and its associated limitations. Collapse

Topics: Unix enterprise architecture

Methods: simulation experiment design artifact coherence analysis longitudinal research