Bibliometric Analysis of Sustainability Information Systems Research

2022 | Americas Conference on Information Systems | Citations: 0

Authors: El-Rayes, Nesreen; Abraham, Thomas; Dao, Viet

Abstract: As IS literature on Sustainability has evolved significantly, reviews of the li ... Expand

Abstract: As IS literature on Sustainability has evolved significantly, reviews of the literature tend to focus more on Green IT, and Green IS research and utilize a smaller body of literature. To help enhance understanding of the evolution of IS and Sustainability literature, this research examines an extensive body of knowledge. We used a comprehensive list of keywords to retrieve research related to Green IT, Green IS, and Sustainability IS. We searched for work beyond a limited list of outlets using the SCOPUS database with an extended timeframe. We then conducted a bibliometric analysis using text-mining tools to provide a clearer view of the field and its themes, thus it helps address what, where, and when questions about the nature of research in SIS literature, providing insights on what topics have been studied, where the research originated from, how the body of research has evolved over the years, and suggested future research. Collapse

Topics: green IS database system blockchain internet of things mobile system

Methods: natural language processing VOSviewer bibliometric analysis network analysis cluster analysis

Early Detection of Rumor Veracity in Social Media

2019 | HICSS | Citations: 0

Authors: Dang, Anh; Moh’d, Abidalrahman; Islam, Aminul; Milios, Evangelos

Abstract: Rumor spread has become a significant issue in online social networks (OSNs). To ... Expand

Abstract: Rumor spread has become a significant issue in online social networks (OSNs). To mitigate and limit the spread of rumors and its detrimental effects, analyzing, detecting and better understanding rumor dynamics is required. One of the critical steps of studying rumor spread is to identify the level of the rumor truthfulness in its early stage. Understanding and identifying the level of rumor truthfulness helps prevent its viral spread and minimizes the damage a rumor may cause. In this research, we aim to debunk rumors by analyzing, visualizing, and classifying the level of rumor truthfulness from a large number of users that actively engage in rumor spread. First, we create a dataset of rumors that belong to one of five categories: “False”, “Mostly False”, “True”, “Mostly True”, and “Half True”. This dataset provides intrinsic characteristics of a rumor: topics, user’s sentiment, network structural and content features. Second, we analyze and visualize the characteristics of each rumor category to better understand its features. Third, using theories from social science and psychology, we build a feature set to classify those rumors and identify their truthfulness. The evaluation results on our new dataset show that the approach could effectively detect the truth of rumors as early as seven days. The proposed approach could be used as a valuable tool for existing fact-checking websites, such as Snopes.com or Politifact.com, to detect the veracity of rumors in its early stage automatically and educate OSN users to have a well-informed decision-making process. Collapse

Topics: false rumor Twitter decision making Reddit website

Methods: sentiment analysis machine learning experiment OpenNLP term frequency–inverse document frequency

Theories: psychological theory social sciences theory

Visual Interactive Comparison of Part-of-Speech Models for Domain Adaptation

2019 | HICSS | Citations: 0

Authors: John, Markus; Heimerl, Florian; Sudra, Constanze; Ertl, Thomas

Abstract: Interactive visual analysis of documents relies critically on the ability of mac ... Expand

Abstract: Interactive visual analysis of documents relies critically on the ability of machines to process and analyze texts. Important techniques for text processing include text summarization, classification, or translation. Many of these approaches are based on part-of-speech tagging, a core natural language processing technique. Part-ofspeech taggers are typically trained on collections of modern newspaper, magazine, or journal articles. They are known to have high accuracy and robustness when applied to contemporary newspaper style texts. However, the performance of these taggers deteriorates quickly when applying them to more domain specific writings, such as older or even historical documents. Large training sets tend to be scarce for these types of texts due to the limited availability of source material and costly digitization and annotation procedures. In this paper, we present an interactive visualization approach that facilitates analysts in determining part-of-speech tagging errors by comparing several standard part-of-speech tagger results graphically. It allows users to explore, compare, evaluate, and adapt the results through interactive feedback in order to obtain a new model, which can then be applied to similar types of texts. A use case shows successful applications of the approach and demonstrates its benefits and limitations. In addition, we provide insights generated through expert feedback and discuss the effectiveness of our approach. Collapse

Topics: usability

Methods: part of speech tagging computational algorithm natural language processing CoreNLP cross sectional research

Mining and representing unstructured nicotine use data in a structured format for secondary use

2019 | HICSS | Citations: 0

Authors: Ngwenya, Mandlenkosi; Bankole, Felix

Abstract: The objective of this study was to use rules, NLP and machine learning for addre ... Expand

Abstract: The objective of this study was to use rules, NLP and machine learning for addressing the problem of clinical data interoperability across healthcare providers. Addressing this problem has the potential to make clinical data comparable, retrievable and exchangeable between healthcare providers. Our focus was in giving structure to unstructured patient smoking information. We collected our data from the MIMIC-III database. We wrote rules for annotating the data, then trained a CRF sequence classifier. We obtained an f-measure of 86%, 72%, 69%, 80%, and 12% for substance smoked, frequency, amount, temporal, and duration respectively. Amount smoked yielded a small value due to scarcity of related data. Then for smoking status we obtained an fmeasure of 94.8% for non-smoker class, 83.0% for current-smoker, and 65.7% for past-smoker. We created a FHIR profile for mapping the extracted data based on openEHR reference models, however in future we will explore mapping to CIMI models. Collapse

Topics: healthcare data database system information system use operating system Microsoft Windows

Methods: machine learning computational algorithm natural language processing experiment word embedding

Machine Learning Techniques for Annotations of Large Financial Text Datasets

2019 | Americas Conference on Information Systems | Citations: 0

Authors: Roberts, Jesse; Volk, Matthias; Neumann, Robert; Turowski, Klaus

Abstract: Today, a number of automated methods exist to augment predictive models with an ... Expand

Abstract: Today, a number of automated methods exist to augment predictive models with annotations when working on huge collections of unstructured texts. One such method involves the use of machine learning techniques. This work seeks to investigate the use of those techniques for annotations on UIMA -a framework used for the analysis of unstructured data. By using the design science research methodology, an annotator pipeline is created as the main artifact. It takes news and blog articles as input and extracts entities, which are then annotated with a sentiment. Concurrently a demonstration is taking place, using a set of 12,480 gold annotated documents on German car manufacturers as training data. By harnessing a multitude of validations methods and machine learning algorithms, all results are thoroughly tested and evaluated. As a result, this work provides a blueprint for research into the use of UIMA and machine learning techniques for domain constrained datasets. Collapse

Topics: decision making blog Apache Solr

Methods: computational algorithm machine learning experiment support vector machine literature sample

Twitter user geolocation using web country noun searches

2019 | Decision Support Systems | Citations: 3

Authors: Zola, Paola; Cortez, Paulo; Carpita, Maurizio

Abstract: Several Web and social media analytics require user geolocation data. Although T ... Expand

Abstract: Several Web and social media analytics require user geolocation data. Although Twitter is a powerful source for social media analytics, its user geolocation is a nontrivial task. This paper presents a purely word distribution method for Twitter user country geolocation. In particular, we focus on the frequencies of tweet nouns and their statistical matches with Google Trends world country distributions (GTN method). Several experiments were conducted, using a recently created dataset of 744,830 tweets produced by 3298 users from 54 countries and written in 48 languages. Overall, the proposed GTN approach is competitive when compared with a state-of-the-art world distribution geolocation method. To reduce the number of Google Trends queries, we also tested a machine learning variant (GTN2) that is capable of matching the GTN responses with an 80% accuracy while being much faster than GTN. Collapse

Topics: Twitter Google+ Python privacy tablet

Methods: machine learning support vector machine experiment computational algorithm machine learning method

Counterfeit product detection: Bridging the gap between design science and behavioral science in information systems research

2017 | Decision Support Systems | Citations: 3

Authors: Wimmer, Hayden; Yoon, Victoria Y.

Abstract: In IS research, there is a dichotomy where design science and behavioral science ... Expand

Abstract: In IS research, there is a dichotomy where design science and behavioral science are distinct research paradigms. IS researchers should view these paradigms as complementary with research drawing upon the strengths of both, yet few have done so. This work demonstrates how design science and behavioral science can be united in IS research via counterfeit product detection based on product reviews in an online marketplace. Product authenticity in the online marketplace is a common issue plaguing consumers. The decision process involved in determining product authenticity is lengthy and complex. Despite the pressing need for an automatic authenticity rating system for online shopping, little research has been done to develop such a system and assess its effects on consumer purchase behavior. To respond to this need, our study develops a design artifact, called OnCDS, to automatically calculate the likelihood that a product is counterfeit based on online customer reviews. Drawing upon lexicon-based sentiment analysis approaches and TF-IDF as kernel theories for our design, we employ web scraping, natural language processing, and topic analysis methods to process customer reviews and calculate the counterfeit score of a product. In assessing the effects of OnCDS on consumer behavior, we develop a research model that encompasses trust and perceived risk based on the valence framework. Results show that our design artifact's efficacy is validated and that the counterfeit score affects perceived risk and trust, which in turn influences attitude toward purchase. Collapse

Topics: consumer behavior electronic market data quality information retrieval electronic commerce

Methods: design artifact sentiment analysis term frequency–inverse document frequency product review design science

Theories: information systems theory behavioral theory

Polarity classification using structure-based vector representations of text

2015 | Decision Support Systems | Citations: 0

Authors: Hogenboom, Alexander; Frasincar, Flavius; de Jong, Franciska; Kaymak, Uzay

Abstract: The exploitation of structural aspects of content is becoming increasingly popul ... Expand

Abstract: The exploitation of structural aspects of content is becoming increasingly popular in rule-based polarity classification systems. Such systems typically weight the sentiment conveyed by text segments in accordance with these segments' roles in the structure of a text, as identified by deep linguistic processing. Conversely, state-of-the-art machine learning polarity classifiers typically aim to exploit patterns in vector representations of texts, mostly covering the occurrence of words or word groups in these texts. However, since structural aspects of content have been shown to contain valuable information as well, we propose to use structure-based features in vector representations of text. We evaluate the usefulness of our novel features on collections of English reviews in various domains. Our experimental results suggest that, even though word-based features are indispensable to good polarity classifiers, structure-based sentiment information provides valuable additional guidance that can help significantly improve the polarity classification performance of machine learning classifiers. The most informative features capture the sentiment conveyed by specific rhetorical elements that constitute a text's core or provide crucial contextual information. Collapse

Topics: online review knowledge representation accounting decision making Java

Methods: sentiment analysis product review machine learning WordNet support vector machine

Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams

2013 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Pervin, Nargis; Fang, Fang; Datta, Anindya; Dutta, Kaushik; Vandermeer, Debra

Abstract: Social networks, such as Twitter, can quickly and broadly disseminate news and m ... Expand

Abstract: Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both real-world events and cultural trends. Such networks are often the best sources of up-to-the-minute information, and are therefore of considerable commercial and consumer interest. The trending topics that appear first on these networks represent an answer to the age-old query “what are people talking about?” Given the incredible volume of posts (on the order of 45,000 or more per minute), and the vast number of stories about which users are posting at any given time, it is a formidable problem to extract trending stories in real time. In this article, we describe a method and implementation for extracting trending topics from a high-velocity real-time stream of microblog posts. We describe our approach and implementation, and a set of experimental results that show that our system can accurately find “hot” stories from high-rate Twitter-scale text streams. Collapse

Topics: Twitter database system microblog social network Linux

Methods: computational algorithm experiment cluster analysis literature study machine learning

Feature selection for sentiment analysis based on content and syntax models

2012 | Decision Support Systems | Citations: 11

Authors: Duric, Adnan; Song, Fei

Abstract: Recent solutions for sentiment analysis have relied on feature selection methods ... Expand

Abstract: Recent solutions for sentiment analysis have relied on feature selection methods ranging from lexicon-based approaches where the set of features are generated by humans, to approaches that use general statistical measures where features are selected solely on empirical evidence. The advantage of statistical approaches is that they are fully automatic, however, they often fail to separate features that carry sentiment from those that do not. In this paper we propose a set of new feature selection schemes that use a Content and Syntax model to automatically learn a set of features in a review document by separating the entities that are being reviewed from the subjective expressions that describe those entities in terms of polarities. By focusing only on the subjective expressions and ignoring the entities, we can choose more salient features for document-level sentiment analysis. The results obtained from using these features in a maximum entropy classifier are competitive with the state-of-the-art machine learning approaches. Collapse

Topics: online review

Methods: experiment machine learning product review computational algorithm sentiment analysis