Facebook messenger bots and their application for business

Vuković, Dijana; Dujlovic, Igor

doi:10.1109/telfor.2016.7818926

Cited by 12 publications

(4 citation statements)

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“…Given these benefits, chatbots have been widely used in marketing, not least in the health care, hospitality, entertainment, banking, and e-commerce sectors (Vukovic and Dujlovic 2017). According to a report by Insider Intelligence (2021), the chatbot market is expected to grow from US$2.8 billion in 2021 to US$142 billion in 2024.…”

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confidence: 99%

Chatbots in Marketing: A Literature Review Using Morphological and Co-Occurrence Analyses

Ramesh¹,

Chawla²

2022

Journal of Interactive Marketing

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Chatbots have become common in marketing-related applications, providing 24/7 service, engaging customers in humanlike conversation, and reducing employee workload in handling customer calls. However, the academic literature on the use of chatbots in marketing remains sparse and scattered across disciplines. The present study combines morphological analysis and co-occurrence analysis to bring structure to this area and to identify relevant research gaps. Morphological analysis divides a problem into pertinent and clearly distinguishable components, namely dimensions (at an abstract level) and variants (at a concrete level). A Zwicky box (a cross-variant matrix of dimensions) is then constructed to identify future research opportunities. Here, the authors obtain 11 dimensions and 264 variants. To eliminate inconsistent configurations (i.e., combinations of variants across dimensions), they perform a cross-consistency assessment and identify potential research gaps. To increase objectivity in the selection of relevant gaps, the authors use VOSviewer software to conduct a co-occurrence analysis of the variants.

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confidence: 99%

Chatbots in Marketing: A Literature Review Using Morphological and Co-Occurrence Analyses

Ramesh¹,

Chawla²

2022

Journal of Interactive Marketing

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“…[12] where thinking is a human ability of deciding the right answer for a question. The imitation game was suggested by Turing as a method to directly avoid the question and to specify a measurement evaluating the degree of similarity between the machine and a human [15]. Chatbots continue to grow in popularity with 80% of businesses expecting to be using one by 2020 [15].…”

Section: B Chatbots Historymentioning

confidence: 99%

“…The imitation game was suggested by Turing as a method to directly avoid the question and to specify a measurement evaluating the degree of similarity between the machine and a human [15]. Chatbots continue to grow in popularity with 80% of businesses expecting to be using one by 2020 [15]. There are now chatbots for multiple uses such as Customer Service (Twyla), time-sheets (Nika), Expenses (Acebot) and feedback (Wizu).…”

Section: B Chatbots Historymentioning

confidence: 99%

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Improvement of Chatbots Semantics Using Wit.ai and Word Sequence Kernel: Education Chatbot as a Case Study

Qaffas¹

2019

IJMECS

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Designing interactive question-response systems has become an important challenge in Artificial Intelligence which aims to build a computer program, referred to as Chatbot, able to manage an online humancomputer conversation with natural language. The number of chatbots continues to increase these recent years using different languages, tools and platforms and has been used in several domains, such as marketing, education and medicine. One of the most important issues in designing chatbots is its degree of interoperability with human natural language. In this context, we propose in this work a new messenger chatbot design approach based on Wit.ai and Word Sequence kernel in order to improve semantics. Wit.ai is used to detect contexts and concepts while the Word Sequence Kernel is used as a similarity measure between textual conversations taking into account the order of appearance of words in the conversation. A testing educate chatbot has been build which aims to provide FAQBot system for university students and acts as undergraduate advisor in student information desk. The performance of the proposed chatbot was compared to conventional messenger chatbots and showed better results.

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Public interest in science or bots? Selective amplification of scientific articles on Twitter

Rahman,

Mohammadi,

Alhoori

2024

AJIM

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PurposeWith the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. Since spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public’s lives in the real world, this topic warrants critical study and attention.Design/methodology/approachWe used the Altmetric dataset in combination with data collected through the Twitter Application Programming Interface (API) and the Botometer API. We combined the data into an extensive dataset with academic articles, several features from the article and a label indicating whether the article had excessive bot activity on Twitter or not. We analyzed the data to see the possibility of bot activity based on different characteristics of the article. We also trained machine-learning models using this dataset to identify possible bot activity in any given article.FindingsOur machine-learning models were capable of identifying possible bot activity in any academic article with an accuracy of 0.70. We also found that articles related to “Health and Human Science” are more prone to bot activity compared to other research areas. Without arguing the maliciousness of the bot activity, our work presents a tool to identify the presence of bot activity in the dissemination of an academic article and creates a baseline for future research in this direction.Research limitations/implicationsWe considered the features available from the Altmetric dataset. It can be exciting research to extract additional features about the authors of the article, the location of the publication, international collaboration and other demographic features of the authors to see the relation of these features with bot activity.Practical implicationsSince public interest in scientific findings can shape the decisions of policymakers, it is essential to identify the possibility of bot activity in the dissemination of any given scholarly article. Without arguing whether the social bots are good or bad and without arguing about the validity of a scholarly article, our work proposes a tool to interpret the public interest in an article by identifying the possibility of bot activity toward an article. This work publishes the models and data generated through the study and provides a benchmark and guideline for future works in this direction.Originality/valueWhile the majority of the existing research focuses on identifying and preventing bot activity on social media, our work is novel in predicting the possibility of bot activity in the dissemination of an academic article using Altmetric metadata for the article. Little work has been performed in this specific area, and the models developed from our research give policymakers and the public a tool to interpret and understand the public interest in a scientific publication with appropriate caution.

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Facebook messenger bots and their application for business

Cited by 12 publications

References 1 publication

Chatbots in Marketing: A Literature Review Using Morphological and Co-Occurrence Analyses

Chatbots in Marketing: A Literature Review Using Morphological and Co-Occurrence Analyses

Improvement of Chatbots Semantics Using Wit.ai and Word Sequence Kernel: Education Chatbot as a Case Study

Public interest in science or bots? Selective amplification of scientific articles on Twitter

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