Detection of Stock Manipulation Influencer Content using Supervised Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Haase, Frederic; Rath, Oliver; Lauten, Julia; Schoder, Detlef

Abstract: In recent years, social media influencers have emerged as key players in stock ... Expand

Abstract: In recent years, social media influencers have emerged as key players in stock manipulation schemes. Despite their growing impact, methods to detect such activities remain scarcely explored. In this study, we examine the social media content of stock manipulation influencers (SMIs) implicated in a $100 million fraud case by the U.S. Securities and Exchange Commission (SEC) in 2022. Leveraging natural language processing (NLP) techniques, we first investigate the linguistic characteristics present in the social media content published by SMIs. Next, we develop and evaluate supervised learning models to detect manipulative content. Our results have significant implications for investors, regulators, and the broader financial community. They reveal the unique linguistic characteristics of SMI content and demonstrate the potential of machinelearning and deep-learning-based techniques in advancing fraud detection systems. Collapse

Topics: Twitter social media fraud detection database system social media analytics

Methods: machine learning RoBERTa DistilBERT computational algorithm natural language processing

GDPR Privacy Type Clustering: Motivational Factors for Consumer Data Sharing

2023 | European Conference On Information Systems | Citations: 7

Authors: Hanneke, Björn; Baum, Lorenz; Hinz, Oliver

Abstract: The GDPR introduced restrictive privacy-preserving measures, affecting the dail ... Expand

Abstract: The GDPR introduced restrictive privacy-preserving measures, affecting the daily life of (online) consumers. Moreover, literature shows that privacy preferences are constantly evolving. This is the first study introducing a GDPR exercising-oriented approach to identify consumer privacy types. Based on a representative sample of the German online population, we cluster consumers according to their privacy importance ("intention to act") and GDPR knowledge ("ability to act") and derive four consumer privacy type clusters: fundamentalists, amateurs, pragmatists, and unconcerned. We investigate motivational factors for changing privacy settings and find significant differences between consumers' intentions and actions for selected factors. This provides evidence for the privacy paradox. Contrarily, intentions and actions align for other factors, which supports the hypothesis that actionbased consent might lower the privacy paradox. Finally, we suggest the development of standardized scales and corresponding clustering methodologies for consumer privacy type clustering to increase comparability over time and across populations. Collapse

Topics: privacy legal context General Data Protection Regulation privacy behavior advertising management

Methods: cluster analysis survey archival research k-means clustering survey design

Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry

2022 | Information & Management | Citations: 0

Authors: Chang, Yung-Chun; Ku, Chih-Hao; Nguyen, Duy-Duc Le

Abstract: This study investigates customer satisfaction through aspect-level sentiment ana ... Expand

Abstract: This study investigates customer satisfaction through aspect-level sentiment analysis and visual analytics. We collected and examined the flight reviews on TripAdvisor from January 2016 to August 2020 to gauge the impact of COVID-19 on passenger travel sentiment in several aspects. Till now, information systems, management, and tourism research have paid little attention to the use of deep learning and word embedding techniques, such as bidirectional encoder representations from transformers, especially for aspect-level sentiment analysis. This paper aims to identify perceived aspect-based sentiments and predict unrated sentiments for various categories to address this research gap. Ultimately, this study complements existing sentiment analysis methods and extends the use of data-driven and visual analytics approaches to better understand customer satisfaction in the airline industry and within the context of the COVID-19. Our proposed method outperforms baseline comparisons and therefore contributes to the theoretical and managerial literature. Collapse

Topics: pandemic customer satisfaction visual analytics cloud computing data visualization

Methods: BERT deep learning natural language processing exploratory data analysis sentiment analysis

Theories: construal level theory appraisal theory

Revival of the Cover Letter? Experimental Evidence on the Performance of AI-driven Personality Assessments

2022 | International Conference on Information Systems | Citations: 0

Authors: Kecht, Christoph; Kurschilgen, Michael; Strobel, Magnus

Abstract: Organizations have long been trying to assess job applicants' personality using ... Expand

Abstract: Organizations have long been trying to assess job applicants' personality using self-reported psychometric tests, such as the Big Five personality test. However, these tests are not robust against incentives to pretend having certain desirable traits, for example, the disposition for being a good team player. We test whether machine learning classifiers trained on written self-descriptions, such as cover letters, predict people's true cooperativeness better than psychometric tests. Based on data from a controlled online experiment with 400 participants, we find that -when people have incentives to fake their personality -linguistic classifiers based on self-descriptions significantly outperform psychometric classifiers based on the Big Five. Moreover, we find that a fine-tuned, pre-trained natural language model can detect incentives to fake in people's self-descriptions. While further research is needed to achieve tamper-proof models, our findings illustrate the potential of automated personality tests based on job applicants' cover letters. Collapse

Topics: personality public good Python artificial intelligence Wikipedia

Methods: machine learning experiment BERT psychometrics language model

Theories: big five model

Machine Learning and Survey-based Predictors of InfoSec Non-Compliance

2022 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Marshall, Byron; Curry, Michael; Crossler, Robert E.; Correia, John

Abstract: Survey items developed in behavioral Information Security (InfoSec) research sho ... Expand

Abstract: Survey items developed in behavioral Information Security (InfoSec) research should be practically useful in identifying individuals who are likely to create risk by failing to comply with InfoSec guidance. The literature shows that attitudes, beliefs, and perceptions drive compliance behavior and has influenced the creation of a multitude of training programs focused on improving ones’ InfoSec behaviors. While automated controls and directly observable technical indicators are generally preferred by InfoSec practitioners, difficult-to-monitor user actions can still compromise the effectiveness of automatic controls. For example, despite prohibition, doubtful or skeptical employees often increase organizational risk by using the same password to authenticate corporate and external services. Analysis of network traffic or device configurations is unlikely to provide evidence of these vulnerabilities but responses to well-designed surveys might. Guided by the relatively new IPAM model, this study administered 96 survey items from the Behavioral InfoSec literature, across three separate points in time, to 217 respondents. Using systematic feature selection techniques, manageable subsets of 29, 20, and 15 items were identified and tested as predictors of non-compliance with security policy. The feature selection process validates IPAM's innovation in using nuanced self-efficacy and planning items across multiple time frames. Prediction models were trained using several ML algorithms. Practically useful levels of prediction accuracy were achieved with, for example, ensemble tree models identifying 69% of the riskiest individuals within the top 25% of the sample. The findings indicate the usefulness of psychometric items from the behavioral InfoSec in guiding training programs and other cybersecurity control activities and demonstrate that they are promising as additional inputs to AI models that monitor networks for security events. Collapse

Topics: IT security innovation management data security information security risk artificial intelligence

Methods: computational algorithm survey support vector machine artificial neural network psychometrics

Theories: protection motivation theory

Explainability and fairness of RegTech for regulatory enforcement: Automated monitoring of consumer complaints

2022 | Decision Support Systems | Citations: 0

Authors: Siering, Michael

Abstract: The application of regulatory technology (RegTech) for monitoring comprehensive ... Expand

Abstract: The application of regulatory technology (RegTech) for monitoring comprehensive data sources has gained increased importance. Nevertheless, previous research neglects that the output of RegTech applications has to be explainable and non-discriminatory. Within this study, we propose design principles and features for a RegTech approach which provides automated assessments of financial consumer complaints. We follow three main design principles. First, we build upon information diagnosticity theory to address the need for explainable classifica­ tions empowering regulators to justify their actions. Second, we consider a bag-of-words representation and ensemble learning to ensure high classification accuracy. Third, we take into account author characteristics to avoid discriminating classifications. We evaluate our approach in the financial services industry and show its value for identifying consumer complaints resulting in monetary compensations. The proposed design principles and features are highly relevant for regulators, corporations as well as consumers. Collapse

Topics: online review database system user-generated content experience good information overload

Methods: design principle machine learning design requirement McNemar's test kernel theory

On the platform but will they buy? Predicting customers' purchase behavior using deep learning

2021 | Decision Support Systems | Citations: 7

Authors: Chaudhuri, Neha; Gupta, Gaurav; Vamsi, Vallurupalli; Bose, Indranil

Abstract: A thorough understanding of online customer’s purchase behavior will directly bo ... Expand

Abstract: A thorough understanding of online customer’s purchase behavior will directly boost e-commerce business performance. Existing studies have overtly focused on purchase intention and used sales rank as a natural proxy, which however has limited business application. Additionally, intention to purchase does not necessarily convert to actual retail purchases. We aim to further our understanding of online customer’s purchase behavior for an ecommerce platform by predicting the same using deep learning techniques, on a large multidimensional data sample of more than 50,000 unique web sessions. This study used two distinct sets of variables, i.e., platform engagement and customer characteristics, as key predictors of online purchases by retail customers. We further compared the predictive capability of our deep learning method with other widely used machine learning techniques for prediction, including Decision Tree, Random Forest, Support Vector Machines, and Artificial Neural Networks. We found that the deep learning technique outperformed the machine learning techniques when applied to the same dataset. These analyses will help platform designers plan for more platform engage­ ments while simultaneously expanding the academic understanding of purchase prediction for online e-com­ merce platforms. Collapse

Topics: electronic commerce missing data open source Google+ lean manufacturing

Methods: machine learning deep learning sensitivity analysis descriptive statistic support vector machine

Music intelligence: Granular data and prediction of top ten hit songs

2021 | Decision Support Systems | Citations: 1

Authors: Kim, Seon Tae; Oh, Joo Hee

Abstract: In the music market, superstars significantly dominate the market share, while p ... Expand

Abstract: In the music market, superstars significantly dominate the market share, while predicting the top hit songs is notoriously difficult. The music intelligence technology, retrieving and utilizing granular acoustic features of songs, provides opportunities to improve the prediction of top hit songs. Using data on 6209 unique songs that appeared in the weekly Billboard Hot 100 charts from 1998 to 2016, especially acoustic features provided by Spotify, we investigate empirically how the top-10-hit-songs likelihood prediction is improved by acoustic fea­ tures. We find that some acoustic features (e.g., danceability, happiness, and some metrics of timbre and pitch) significantly improve the model’s ability to predict the top-10-hit-songs probability. These results suggest that the granular data, provided by the music intelligence technology, carries a substantial predictive value in the era of online music streaming. Collapse

Topics: digital service Spotify music streaming service logistics management internet technology

Methods: computational algorithm machine learning descriptive statistic generalized linear model McNemar's test

Leveraging online review platforms to support public policy: Predicting restaurant health violations based on online reviews

2021 | Decision Support Systems | Citations: 1

Authors: Siering, Michael

Abstract: Restaurant health inspections aim at identifying health violations and shall red ... Expand

Abstract: Restaurant health inspections aim at identifying health violations and shall reduce the risk that restaurant vis­ itors suffer from foodborne illness. Nevertheless, regulatory authorities’ resources are limited, so an efficient mechanism that supports scheduling of health inspections is necessary. We build upon information efficiency theory and investigate whether information extracted from online review platforms is useful to predict restaurant health violations. Furthermore, we examine how the expectation disconfirmation bias impacts classification performance. Analyzing a large sample of health inspections, corresponding online reviews and restaurant visitor data, we propose and evaluate different predictive models. We find that classifiers specifically taking into ac­ count information from online review platforms outperform different baseline approaches. We thus show that online reviews encompass private information indicating strong information efficiency. Furthermore, we observe that the expectation disconfirmation bias has an influence on classification performance in case of restaurants with a low star rating and with a poor inspection history. An ensemble classifier can help to mitigate this in­ fluence. Thus, online review platforms contain relevant information to predict future health violations. Our results are highly relevant for regulatory authorities, restaurant visitors and restaurant owners. Collapse

Topics: online review user-generated content privacy user expectation decision making

Methods: descriptive statistic machine learning support vector machine computational algorithm McNemar's test

Who Is the Next “Wolf of Wall Street”? Detection of Financial Intermediary Misconduct

2020 | Journal of the Association for Information Systems | Citations: 0

Authors: Lausen, Jens; Clapham, Benjamin; Siering, Michael; Gomber, Peter

Abstract: Financial intermediaries are essential for investors’ participation in financial ... Expand

Abstract: Financial intermediaries are essential for investors’ participation in financial markets. Because of their position within the financial system, intermediaries who commit misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory and warranting theory and making use of self-disclosed data with different levels of external verification, we propose different classifiers to automatically detect financial intermediary misconduct. In particular, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulator-disclosed information and use these data for classifier training and evaluation. We find that self-disclosed information provides valuable input for detecting financial intermediary misconduct. In terms of external verification, our classifiers achieve the best predictive performance when also taking regulator-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators seeking to identify financial intermediary misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system. Collapse

Topics: LinkedIn website data breach social network fraud detection

Methods: machine learning support vector machine sensitivity analysis classification method McNemar's test

Theories: information manipulation theory interpersonal deception theory