Would you please like my tweet?! An artificially intelligent, generative probabilistic, and econometric based system design for popularity-driven tweet content generation

2021 | Decision Support Systems | Citations: 0

Authors: Garvey, Myles D

Abstract: An understudied area in the field of social media research is the design of deci ... Expand

Abstract: An understudied area in the field of social media research is the design of decision support systems that can aid the manager by way of automated message component generation. Recent advances in this form of artificial intelligence has been suggested to allow content creators and managers to transcend their tasks from creation towards editing, thus overcoming a common problem: the tyranny of the blank screen. In this research, we address this topic by proposing a novel system design that will suggest engagement-driven message features as well as automatically generate critical and fully written unique Tweet message components for the goal of maximizing the probability of relatively high engagement levels. Our multi-methods design relies on the use of econometrics, machine learning, and Bayesian statistics, all of which are widely used in the emerging fields of Business and Marketing Analytics. Our system design is intended to analyze Tweet messages for the purpose of generating the most critical components and structure of Tweets. We propose econometric models to judge the quality of written Tweets by way of engagement-level prediction, as well as a generative probability model for the auto-generation of Tweet messages. Testing of our design demonstrates the need to take into account the contextual, semantic, and syntactic features of messages, while controlling for individual user characteristics, so that generated Tweet components and structure maximizes the potential engagement levels. Collapse

Topics: Twitter social media systems design application programming interface decision making

Methods: computational algorithm econometric modeling natural language processing descriptive statistic machine learning

From semantics to pragmatics: where IS can lead in Natural Language Processing (NLP) research

2021 | European Journal of Information Systems | Citations: 0

Authors: Li, Yan; Thomas, Manoj a; Liu, Dapeng

Abstract: Natural Language Processing (NLP) is now widely integrated into web and mobile a ... Expand

Abstract: Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between humans and computers. Although there is a large body of NLP studies published in Information Systems (IS), a comprehensive review of how NLP research is conceptualised and realised in the context of IS has not been conducted. To assess the current state of NLP research in IS, we use a variety of techniques to analyse a literature corpus comprising 356 NLP research articles published in IS journals between 2004 and 2018. Our analysis indicates the need to move from semantics to pragmatics. More importantly, our findings unpack the challenges and assumptions underlying current research trends in NLP. We argue that overcoming these challenges will require a renewed disciplinary IS focus. By proposing a roadmap of NLP research in IS, we draw attention to three NLP research perspectives and present future directions that IS researchers are uniquely positioned to address. Collapse

Topics: database system knowledge representation social media analytics social media information retrieval

Methods: natural language processing qualitative content analysis literature study computational algorithm literature sample

Incorporating Personalization Features in a Hospital-Stay Summary Generation System

2019 | HICSS | Citations: 0

Authors: Acharya, Sabita; Boyd, Andrew D; Cameron, Richard; Lopez, Karen Dunn; Martyn-Nemeth, Pamela; Dickens, Carolyn; Ardati, Amer; Eugenio, Barbara Di

Abstract: Most of the currently available health resources contain vast amount of informat ... Expand

Abstract: Most of the currently available health resources contain vast amount of information that are created by keeping the “general” population in mind, which in reality, might not be useful for anyone. One approach to providing comprehensible health information to patients is to generate summaries that are personalized to each individual. This paper details the design of a personalized hospital-stay summary generation system that tailors its content to the patient’s understanding of medical terminologies and their level of engagement in improving their own health. Our summaries were found to cover around 80% of the health concepts that were considered as important by a doctor or a nurse. An online survey conducted on 150 participants verified that our algorithm’s interpretation of the personalization parameters is representative of that of a larger population. Collapse

Topics: usability personalization

Methods: computational algorithm qualitative interview survey literature study natural language generation

Chatbots at digital Workplaces

2019 | HICSS | Citations: 0

Authors: von Wolff, Raphael Meyer; Hobert, Sebastian; Schumann, Matthias

Abstract: Chatbots become quite hyped in recent times as they can provide an intuitive and ... Expand

Abstract: Chatbots become quite hyped in recent times as they can provide an intuitive and easy-to-use natural language human-computer interface. Nevertheless, they are not yet widespread in enterprises. Corresponding application areas for collaboration at digital workplaces are lacking and prior research contributions on this topic are limited. In this research paper, we aim at surveying the state of the art as well as showing future research topics. Thus, we conducted a structured literature review and showed that only few first research contributions exist. We also outline current potentials and objectives of chatbot applications. In the discussion of the results of our structured literature review, we show that research gaps are present. To tackle the research gaps, we derive open research questions. Collapse

Topics: knowledge base information storage and retrieval system enterprise information system accounting collaborative work

Methods: chatbot structured literature research literature study design principle design artifact

Intelligent Technologies Shaping Business Models for Journalistic Content Provision: A Concept Matrix

2017 | Americas Conference on Information Systems | Citations: 0

Authors: Utasch, Frederic; Loebbecke, Claudia

Abstract: There has been much discussion about how intelligent technologies shape journali ... Expand

Abstract: There has been much discussion about how intelligent technologies shape journalistic content provision. Common buzz words are data-driven and automated journalism. To address this issue, we conduct on a two-phase, systemic literature review in order to develop an integrated Concept Matrix (Webster & Watson 2002) on how intelligent technologies - here natural language generation, predictive intelligence algorithms, and latent semantic indexing - shape the business models of journalistic content provision. We offer insights regarding 'roles of' intelligent technologies and how they drive for' business models as we find that they, will make the difference also in journalism as in many other already or soon digital industries. Collapse

Topics: business model price management competitive advantage robotic advertising management

Methods: literature sample natural language generation literature study latent semantic indexing computational algorithm

A NATURAL LANGUAGE TECHNOLOGY-ENHANCED MOBILE SALES ASSISTANT FOR IN-STORE SHOPPING SITUATIONS

2011 | European Conference On Information Systems | Citations: 0

Authors: Maass, Wolfgang; Kowatsch, Tobias; Janzen, Sabine; Varshney, Upkar

Abstract: Sales talks between customers and sales personnel are efficient and preferred me ... Expand

Abstract: Sales talks between customers and sales personnel are efficient and preferred means for exchanging information that is relevant for purchase decisions on non-commodity products. Dialogs used in sales talks are governed by complex and in many respects conflicting intentions on both sides. While previous Decision Support Systems (DSS) are designed by the principle of congruent intentions of communication partners, we present an approach that extends this by congruent and opposing intentions of communication partners. We use a design methodology for dialog-based purchase DSS that use Natural Language Technologies (NLT) for dynamically creating question-answer-based sales dialogs. It is first shown how dialog schemata are obtained by a field study and evaluated by subjects. In the second part, these schemata are integrated in a Natural Language Technology-enhanced Mobile Sales Assistant (NLT-MSA). The role of NLT-MSA is to take the position of a sales person with the task to balance congruent and conflicting intentions during sales dialogs. Results of a lab experiment (n=54) are discussed by which the use of a NLT-MSA prototype in sales situations were tested. As part of this study, test persons rated application domains for NLT-MSA that will guide future field experiments in the large. Collapse

Topics: decision support system mobile phone perceived ease of use usage intention usability

Methods: design science field study one sample t-test laboratory experiment experiment

Theories: diffusion of innovations theory

Argumentation–based negotiation planning for autonomous agents

2011 | Decision Support Systems | Citations: 0

Authors: Monteserin, Ariel; Amandi, Analía

Abstract: When we negotiate, the arguments uttered to persuade the opponent are not the re ... Expand

Abstract: When we negotiate, the arguments uttered to persuade the opponent are not the result of an isolated analysis, but of an integral view of the problem that we want to agree about. Before the negotiation starts, we have in mind what arguments we can utter, what opponent we can persuade, which negotiation can finish successfully and which cannot. Thus, we plan the negotiation, and in particular, the argumentation. This fact allows us to take decisions in advance and to start the negotiation more confidently. With this in mind, we claim that this planning can be exploited by an autonomous agent. Agents plan the actions that they should execute to achieve their goals. In these plans, some actions are under the agent's control, while some others are not. The latter must be negotiated with other agents. Negotiation is usually carried out during the plan execution. In our opinion, however, negotiation can be considered during the planning stage, as in real life. In this paper, we present a novel approach to integrate argumentation-based negotiation planning into the general planning process of an autonomous agent. This integration allows the agent to take key decisions in advance. We evaluated this proposal in a multiagent scenario by comparing the performance of agents that plan the argumentation and agents that do not. These evaluations demonstrated that performance improves when the argumentation is planned, specially, when the negotiation alternatives increase. Collapse

Topics: human computer interaction artificial intelligence usability decision making

Methods: computational algorithm experiment natural language generation

Design and evaluation of an intelligent decision support system for nuclear emergencies

2005 | Decision Support Systems | Citations: 11

Authors: Papamichail, K. N.; French, S.

Abstract: Intelligent Decision Support Systems (DSSs) use expert systems technology to enh ... Expand

Abstract: Intelligent Decision Support Systems (DSSs) use expert systems technology to enhance the capabilities of decision makers (DMs) in understanding a decision problem and selecting a sound alternative. Because of the people-centred focus of such technologies, it is important not only to assess their technical aspects and overall performance but also to seek the views of potential users. This paper draws from the literature to classify methods for assessing intelligent Decision Support Systems and discusses our experiences in developing, operating and evaluating an intelligent decision support system for nuclear emergencies. The system assists decision makers in the formulation and ranking of alternatives and communicates its recommendation in a natural language form. The application highlights insights from the development process and shortcomings of existing assessment methods. Lessons learned from the study, challenges encountered and recommendations for future practices are discussed. Collapse

Topics: decision support system evaluation criteria expert system decision making knowledge base

Methods: survey experiment experimental group literature study descriptive statistic

The SNePS BDI architecture

1996 | Decision Support Systems | Citations: 0

Authors: Kumar, Deepak

Abstract: Most AI systems are effective either for inference or for acting/planning but no ... Expand

Abstract: Most AI systems are effective either for inference or for acting/planning but not for both. The SNePS BDI architecture uses propositional semantic network representations of beliefs, goals, acts, plans, and a representational concept called transformers. Transformers help capture a unified approach to acting and inference. They can be used to represent reasoning rules, reactive desires, desires for acquiring knowledge, preconditions and effects of actions as well as plan decompositions. A rational engine module operates on these representations and is responsible for the agent's reasoning and acting behavior. SNeRE, the SNePS rational engine, employs a quasi-concurrent message passing scheme, implements the underlying logic as well as the action theory, has forward, backward and bidirectional inference and acting. It also incorporates the notions of truth maintenance and spreading activation. Collapse

Topics: artificial intelligence knowledge representation usability user interface knowledge acquisition

Methods: ontological modelling natural language generation design artifact

Theories: action theory speech act theory

The Generalization of a Decision Simulation

1995 | Americas Conference on Information Systems | Citations: 1

Authors: Slade, Stephen; Fish, Michael; Arunkundram, Ravi

Abstract: This paper discusses a research project in which an artificial intelligence deci ... Expand

Abstract: This paper discusses a research project in which an artificial intelligence decision making model in the domain of politics is applied to business decisions in general and an information technology decisions in particular. Collapse

Topics: decision making artificial intelligence

Methods: simulation natural language generation machine learning design principle

Theories: decision theory