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1. Ask the Experts: What Should Be on an IoT Privacy and Security Label?

   https://doi.org/10.1109/SP40000.2020.00043  

   Emami-Naeini, Pardis ; Agarwal, Yuvraj ; Faith Cranor, Lorrie ; Hibshi, Hanan ( May 2020 , The 41st IEEE Symposium on Security and Privacy)

   Information about the privacy and security of Internet of Things (IoT) devices is not readily available to consumers who want to consider it before making purchase decisions. While legislators have proposed adding succinct, consumer accessible, labels, they do not provide guidance on the content of these labels. In this paper, we report on the results of a series of interviews and surveys with privacy and security experts, as well as consumers, where we explore and test the design space of the content to include on an IoT privacy and security label. We conduct an expert elicitation study by following a three-round Delphi process with 22 privacy and security experts to identify the factors that experts believed are important for consumers when comparing the privacy and security of IoT devices to inform their purchase decisions. Based on how critical experts believed each factor is in conveying risk to consumers, we distributed these factors across two layers—a primary layer to display on the product package itself or prominently on a website, and a secondary layer available online through a web link or a QR code. We report on the experts’ rationale and arguments used to support their choice of factors. Moreover, to study how consumers would perceive the privacy and security information specified by experts, we conducted a series of semi-structured interviews with 15 participants, who had purchased at least one IoT device (smart home device or wearable). Based on the results of our expert elicitation and consumer studies, we propose a prototype privacy and security label to help consumers make more informed IoTrelated purchase decisions. 

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2. Scalable Demand-Aware Recommendation

   Yi, Jinfeng ; Hsieh, Cho-Jui ; Varshney, Kush ; Zhang, Lijun ; Li, Yao ( October 2017 , Neural Information Processing Systems (NIPS))

   Recommendation for e-commerce with a mix of durable and nondurable goods has characteristics that distinguish it from the well-studied media recommendation problem. The demand for items is a combined effect of form utility and time utility, i.e., a product must both be intrinsically appealing to a consumer and the time must be right for purchase. In particular for durable goods, time utility is a function of inter-purchase duration within product category because consumers are unlikely to purchase two items in the same category in close temporal succession. Moreover, purchase data, in contrast to rating data, is implicit with non-purchases not necessarily indicating dislike. Together, these issues give rise to the positive-unlabeled demand-aware recommendation problem that we pose via joint low-rank tensor completion and product category inter-purchase duration vector estimation. We further relax this problem and propose a highly scalable alternating minimization approach with which we can solve problems with millions of users and millions of items in a single thread. We also show superior prediction accuracies on multiple real-world datasets. 

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3. Aspect Extraction from Online Consumer Reviews with WordNet-Guided Continuous-Space Language

   Tao, J. ; Zhou, L. ; Feeney, C. ( December 2018 , The 28th Annual Workshop on Information Technologies and Systems)

   Online consumer reviews contain rich yet implicit information regarding consumers’ preferences for specific aspects of products/services. Extracting aspects from online consumer reviews has been recognized as a valuable step in performing fine-grained analytical tasks (e.g. aspect-based sentiment analysis). Extant approaches to aspect extraction are dominated by discrete models. Despite explosive research interests in continuous-space language models in recent years, these models have yet to be explored for the task of extracting product/service aspects from online consumer reviews. In addition, previous continuous-space models for information extraction have largely overlooked the role of semantic information embedded in texts. In this study, we propose an approach of aspect extraction that leverages semantic information from WordNet in conjunction of building continuous-space language models from review texts. The experiment results with online restaurant reviews demonstrate that the WordNet-guided continuous-space language models outperform both discrete models and continuous-space language models without incorporating the semantic information. The research findings have important implications for understanding consumer preferences and improving business performances. 

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4. YOSM: A New Yoruba Sentiment Corpus for Movie Reviews

   Shode, I. ( April 2022 , . In Proceedings of the 3rd Workshop on African Natural Language Processing, co-located with International Conference on Learning Representations (ICLR) 2022)

   Sentiment Analysis is a popular text classification task in natural language processing. It involves developing algorithms or machine learning models to determine the sentiment or opinion expressed in a piece of text. The results of this task can be used by business owners and product developers to understand their consumers’ perceptions of their products. Asides from customer feedback and product/service analysis, this task can be useful for social media monitoring (Martin et al., 2021). One of the popular applications of sentiment analysis is for classifying and detecting the positive and negative sentiments on movie reviews. Movie reviews enable movie producers to monitor the performances of their movies (Abhishek et al., 2020) and enhance the decision of movie viewers to know whether a movie is good enough and worth investing time to watch (Lakshmi Devi et al., 2020). However, the task has been under-explored for African languages compared to their western counterparts, ”high resource languages”, that are privileged to have received enormous attention due to the large amount of available textual data. African languages fall under the category of the low resource languages which are on the disadvantaged end because of the limited availability of data that gives them a poor representation (Nasim & Ghani, 2020). Recently, sentiment analysis has received attention on African languages in the Twitter domain for Nigerian (Muhammad et al., 2022) and Amharic (Yimam et al., 2020) languages. However, there is no available corpus in the movie domain. We decided to tackle the problem of unavailability of Yoru`ba´ data for movie sentiment analysis by creating the first Yoru`ba´ sentiment corpus for Nollywood movie reviews. Also, we develop sentiment classification models using state-of-the-art pre-trained language models like mBERT (Devlin et al., 2019) and AfriBERTa (Ogueji et al., 2021). 

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5. Valuation and Collaboration among Craft Brewers: Early Insights from an Analysis of Consumer Ratings

   McInerney, Paul-Brian ( July 2018 , International Sociological Association Annual Meeting)

   null (Ed.)

   In pursuing the problem of (e)valuation in sociology, this paper is concerned with the collaborative dynamics that create value. Based on a unique dataset of 225 collaborations among craft brewers, we analyze which combinations of organizations increase consumer ratings of the beers produced. Craft brewing provides a fascinating laboratory for the study of how consumers rate the products of collaborative formations. Collaborations between craft brewers result in one-off products, beers that exist for a limited period of time, rarely to be produced again. Such collaborations occur frequently and exist over the course of several weeks or months, until the product is brewed, packaged, and distributed. At that point, collaborators return to their daily brewing operations. With the help of several research assistants, we created a dataset of craft brewer all collaborations in which at least one brewer was located in Illinois. The data are compiled from websites on which consumers rate beers: RateBeer and Untappd. Initial analyses shows that brewers that follow certain strategies are likely to collaborate with others pursuing certain strategies. For example, we find that brewers that engage in serial hook-ups are likely to do so with brewers that are outside of their core networks while monogamists are likely to seek out those who engage in serial hook-ups. These strategies have consequences for the valuation of their efforts. The products of craft brewers who collaborate with those outside of their core networks consistently receive higher consumer ratings than other forms of collaboration. We suspect that certain collaborative arrangements facilitate authenticity or novelty in the marketplace, which consumers reward with higher ratings. We plan to test hypotheses related to whether authenticity or novelty are driving changes in consumer valuations of products. 

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