Toward Fair Recommendation in Two-sided Platforms

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. However, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed FairRec algorithm guarantees Maxi-Min Share of exposure for the producers, and Envy-Free up to One Item fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus ) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.

Possibilities and Challenges of Online Education in India During the COVID-19 Pandemic

The undergraduate and postgraduate studies of colleges and universities in India have been affected badly amidst the lockdowns for COVID-19 pandemic. The Government has insisted to start the academic activity through online platforms. The biggest concern for the academic institutions now is to select an appropriate e-learning platform. This paper compares different features and facilities available in some widely used online platforms and analyze their suitability from the perspective of socio-economic constraints of students in India. A generic framework for conducting online classes is described that meets the special requirements of the unprivileged students. Some strategic plans to overcome the challenges are identified and suggested. A technical solution for implementation of time-bound assessment module is also proposed.

Teachers' Perception of the Phenomenon of Cyberbullying During the Covid-19 Pandemic

Cyberbullying is a negative social phenomenon that takes place online. It consists of harassing technology users through various means and various platforms. Frequent exposure to this phenomenon can cause emotional, mental and social problems for victims, witnesses and aggressors. In the current pandemic context, when education has shifted to the online environment, and students spend a significant amount of time using different devices and online platforms, the number of cyberbullying cases is constantly increasing. There is currently little research describing how this phenomenon influenced online aggression. In preventing and eliminating this phenomenon, teachers play an important role, due to the time they spend with students and the impact they can have on them. In order to identify teachers' perceptions of this phenomenon during the pandemic and how they manage it in the classroom, we conducted a qualitative research on 10 teachers from primary and secondary schools. The results of the research were interesting and offered a new perspective on this phenomenon during Covid-19 crisis.

USING A MULTI-CRITERIA DECISION-MAKING MODEL TO EVALUATE AND SELECT AN E-COMMERCE PLATFORM

The COVID-19 pandemic has led to disruptions in consumers' lifestyles and purchases, as well as businesses' online business models. Online platforms are increasingly used for shopping purposes. To evaluate and choose an e-commerce platform requires using many criteria and decision makers. Therefore, the process of evaluating and selecting an e-commerce platform is viewed as a multi-criteria decision-making problem. The objective of this study is to develop a multi-criteria decision-making model to help consumers evaluating the e-commerce platforms. In the proposed model, the ratings of alternatives and the weights of the criteria are evaluated using the linguistic variable. Simulation examples are used to show the effectiveness of the model in practice. Keywords: Fuzzy TOPSIS, E-Commerce Platform, Mcdm, Fuzzy Sets.

The phantom steering effect in Q&A websites

Virtual rewards, such as badges, are commonly used in online platforms as incentives for promoting contributions from a userbase. It is widely accepted that such rewards “steer” people’s behaviour towards increasing their rate of contributions before obtaining the reward. This paper provides a new probabilistic model of user behaviour in the presence of threshold rewards, such a badges. We find, surprisingly, that while steering does affect a minority of the population, the majority of users do not change their behaviour around the achievement of these virtual rewards. In particular, we find that only approximately 5–30% of Stack Overflow users who achieve the rewards appear to respond to the incentives. This result is based on the analysis of thousands of users’ activity patterns before and after they achieve the reward. Our conclusion is that the phenomenon of steering is less common than has previously been claimed. We identify a statistical phenomenon, termed “Phantom Steering”, that can account for the interaction data of the users who do not respond to the reward. The presence of phantom steering may have contributed to some previous conclusions about the ubiquity of steering. We conduct a qualitative survey of the users on Stack Overflow which supports our results, suggesting that the motivating factors behind user behaviour are complex, and that some of the online incentives used in Stack Overflow may not be solely responsible for changes in users’ contribution rates.

Partidos conectivos durante la pandemia. La estrategia de comunicación de Podemos en Twitter

Desde el punto de vista comunicativo, los partidos conectivos se definen por su inclinación hacia modelos participativos donde los flujos conversacionales posean una estructura horizontal y bidireccional entre la élite de la formación y el activismo de base. Esto es posible gracias a que ceden parte de su organización a herramientas de la Web. Dentro de esta taxonomía se encuentra el partido español Unidas-Podemos como ejemplo de partido conectivo. En esta investigación se analiza la vertiente comunicativa externa, basada en el uso de redes sociales online, de los principales partidos políticos españoles durante el periodo de Estado de Alarma provocado por el COVID-19. Para ello, se observará la tasa de interacción y respuestas en Twitter de los representantes del Congreso de los Diputados. Los resultados muestran que los patrones comunicativos a nivel externo de Unidas-Podemos responden a criterios verticales, propios de los partidos convencionales. From a communicational point of view, connective parties are based on horizontal and bidirectional structure of conversational flows between the elite of the formation and the party activists. This is possible because they delegate part of their organization to digital and online platforms. In this paper we use the Spanish party Unidas-Podemos as a case of study of connective parties. Drawing upon the Twitter response and interaction rate between Spanish representatives and citizens, this research analyzes the external communicative aspect of the main Spanish political parties during the Lockdown caused by COVID-19. The results show that the external communication patterns of Unidas-Podemos respond to vertical criteria, typical of conventional parties.

Product Ranking on Online Platforms

On online platforms, consumers face an abundance of options that are displayed in the form of a position ranking. Only products placed in the first few positions are readily accessible to the consumer, and she needs to exert effort to access more options. For such platforms, we develop a two-stage sequential search model where, in the first stage, the consumer sequentially screens positions to observe the preference weight of the products placed in them and forms a consideration set. In the second stage, she observes the additional idiosyncratic utility that she can derive from each product and chooses the highest-utility product within her consideration set. For this model, we first characterize the optimal sequential search policy of a welfare-maximizing consumer. We then study how platforms with different objectives should rank products. We focus on two objectives: (i) maximizing the platform’s market share and (ii) maximizing the consumer’s welfare. Somewhat surprisingly, we show that ranking products in decreasing order of their preference weights does not necessarily maximize market share or consumer welfare. Such a ranking may shorten the consumer’s consideration set due to the externality effect of high-positioned products on low-positioned ones, leading to insufficient screening. We then show that both problems—maximizing market share and maximizing consumer welfare—are NP-complete. We develop novel near-optimal polynomial-time ranking algorithms for each objective. Further, we show that, even though ranking products in decreasing order of their preference weights is suboptimal, such a ranking enjoys strong performance guarantees for both objectives. We complement our theoretical developments with numerical studies using synthetic data, in which we show (1) that heuristic versions of our algorithms that do not rely on model primitives perform well and (2) that our model can be effectively estimated using a maximum likelihood estimator. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

“Emotional Distancing”: Change and Strain in U.S. Young Adult College Students’ Relationships During COVID-19

We analyzed qualitative data from 707 USA college students aged 18–22 in late April 2020 regarding if and how their relationships had changed at the start of the COVID-19 pandemic. Most (69%) participants experienced relationship changes, most of whom (77%) described negative changes: less overall contact, feeling disconnected, and increased tension, some of which was due to conflict over pandemic-related public health precautions. Physical distancing from social contacts also created emotional distancing: it was harder to maintain affective connections via online platforms and within the isolating context of shelter-in-place. Due to emerging adulthood being a sensitive window for social development, the COVID-19 pandemic-induced emotional distancing could have long-term ramifications for this cohort’s relationships over the course of their lives.

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.