Sowmya Karunakaran
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Spam in User Generated Content Platforms: Developing the HaBuT Instrument to Measure User Experience
Erik Brorson
IEEE International Conference on Systems, Man, and Cybernetics, IEEE(2019) (to appear)
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We are in an era of user-generated content (UGC) but our understanding of the impact of UGC spam on user experience is limited. Most prior instruments to measure user experience were developed in the context of traditional spam types like web-spam or email-spam. In this paper, we develop the 15 item HaBuT scale, consisting of three sub-scales: Happiness, Burden and Trust that measures user experience with respect to UGC spam. The items in the instrument are analyzed using confirmatory factor analysis with a sample of 700 responses from internet users. This process resulted in an instrument of high reliability and validity. The instrument is a valuable tool for researchers and practitioners interested in designing, implementing, and managing systems that rely on user-generated content and to those studying the impact of UGC spam on user experience. We demonstrate a real-world application of the HaBuT scale by applying it to investigate the impact of review spam on mobile apps users. We present the study results of online experiments with 3300 participants across US, India and South Korea.
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Testing Stylistic Interventions to Reduce Emotional Impact in Content Moderation Workers
Rashmi Ramakrishnan
AAAI Conference on Human Computation and Crowdsourcing 2019(2019), pp. 50-58
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With the rise in user generated content, there is a greater need for content reviews. While machines and technology play a critical role in content moderation, there is still a need for manual reviews. It is known that such manual reviews could be emotionally challenging. We test the effects of simple interventions like grayscaling and blurring to reduce the emotional impact of such reviews. We demonstrate this by bringing in interventions in a live content review setup thus allowing us to maximize external validity. We use a pre-test post-test experiment design and measure review quality, average handling time and emotional affect using the PANAS Scale to measure emotional affect. We find that simple grayscale transformations can provide an easy to implement and use solution that can significantly change the emotional impact of content reviews. We observe however that a full blur intervention can be challenging to reviewers.
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Testing Grayscale Interventions to Reduce Negative Emotional Impact on Manual Reviewers
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Rashmi Ramakrishnan
Symposium on Computing and Mental Health, CHI Workshop '19(2019)
Data Breaches: User Comprehension, Expectations, and Concerns with Handling Exposed Data
Oxana Comanescu
SOUPS: Fourteenth Symposium on Usable Privacy and Security, USENIX(2018)
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Data exposed by breaches persist as a security and privacy threat for Internet users. Despite this, best practices for how companies should respond to breaches, or how to responsibly handle data after it is leaked, have yet to be identified. We bring users into this discussion through two surveys. In the first, we examine the comprehension of 551 participants on the risks of data breaches and their sentiment towards potential remediation steps. In the second survey, we ask 10,212 participants to rate their level of comfort towards eight different scenarios that capture real-world examples of security practitioners, researchers, journalists, and commercial entities investigating leaked data. Our findings indicate that users readily understand the risk of data breaches and have consistent expectations for technical and non-technical remediation steps. We also find that participants are comfortable with applications that examine leaked data---such as threat sharing or a "hacked or not'' service---when the application has a direct, tangible security benefit. Our findings help to inform a broader discussion on responsible uses of data exposed by breaches.
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A system and method are disclosed of verifying authenticity of a message using embedded hyperlinks therein. The method works by checking that the hyperlinks the text points to match the companies/brands the text portrays. The verification makes use of a knowledgebase of company/brand information as a source for retrieving the known sites/pages/domains for those companies/brands. The method then estimates a probabilistic measure of identity checking based on matches between the companies/brands mentioned in the text and the outgoing links from the text. In a variation, the method may additionally use contextual information of the message text to determine authenticity to compute a coverage score. The coverage score is then input into a machine learning model that uses these along with other known indicators of genuineness to determine that the message or Web page is either genuine or not.
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On using prisoner dilemma model to explain bidding decision for computing resources on the Cloud
Business view of cloud: decisions, models and opportunities – a classification and review of research
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Purpose
– This study aims to investigate the decisions related to business aspects of cloud computing and discuss the research density, models/techniques used and identify opportunities for future work.
Design/methodology/approach
– In this paper, 155 research articles shortlisted through a systematic review were analyzed and a classification framework was developed. Using this framework, the research density is discussed and a detailed review of four widely researched decision themes is provided.
Findings
– It was found that current research on business aspects is spread across 23 decision themes. The distribution, however, is skewed with 50 per cent pertaining to just four themes, namely, pricing, markets, sourcing and adoption. Simulation appears to be the preferred modeling approach. Decision themes in consumer behavior, sustainability, auditing and culture offer opportunities for future research.
Research limitations/implications
– The classification framework organizes extant research on applied models and allows researchers to identify potential avenues for application, improvement and development of models to support business decisions. The review is limited to academic articles and does not include industry reports.
Practical implications
– Practitioners can readily understand various perspectives relevant to a decision theme such as pricing or sourcing, seek and use associated models such as simulation, optimization and game theory to support their decision-making.
Originality/value
– Most of the extant review paper deal with cloud computing technology. This study is the first systematic review on the models applied to business aspects of cloud computing. This study provides a classification framework and explicitly lists associated decision themes, models/techniques and opportunities.
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Online Bidding Behaviour And Loss Aversion In Cloud Computing Markets: An Experiment
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The last few years have witnessed a rapid growth in commoditization and consumption of IT services particularly due to the growing acceptance of cloud computing services. This in turn has led to newer forms of pricing the cloud services such as dynamic pricing. Infact, spot pricing, a dynamic pricing scheme has become mainstream. Cloud consumers using these schemes need to place their bids in order to procure computing instances. Most of extant research on cloud dynamic pricing focuses on resource allocation problems and bidding strategies. We identify the need to look at behavioural biases of bidders to bring in a holistic perspective to cloud dynamic pricing discussions. In this paper, we conduct an experiment to elicit the impact of a behavioural bias namely, loss aversion, on a cloud consumer’s bidding behaviour. We discuss the social implications of our result to cloud consumers and the economic implications for cloud providers.
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Bidding Strategies for Spot Instances in Cloud Computing Markets
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In recent times, spot pricing - a dynamic pricing scheme - is becoming increasingly popular for cloud services. This new pricing format, though efficient in terms of cost and resource use, has added to the complexity of decision making for typical cloud computing users. To recommend bidding strategies in spot markets, we use a simulation study to understand the implications that provider-recommended strategies have for cloud users. We use data based on Amazon's Elastic Compute Cloud spot market to provide users with guidelines when considering tradeoffs between cost, wait time, and interruption rates.
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Decisions, Models and Opportunities in Cloud Computing Economics: A Review of Research on Pricing and Markets
RP Sundarraj
Venkataragavan Krishnaswamy
Lecture Notes in Business Information Processing, Springer(2014), pp. 85-99
Strategic Bidding for Cloud Resources under Dynamic Pricing Schemes
RP Sundarraj
IEEE International Symposium on Cloud and Services Computing(2012), pp. 25 - 30
