Shocking the Crowd: The Effect of Censorship Shocks on Chinese Wikipedia

Collaborative crowdsourcing has become a popular approach to organizing work across the globe. Being global also means being vulnerable to shocks -- unforeseen events that disrupt crowds -- that originate from any country. In this study, we examine changes in collaborative behavior of editors of Chinese Wikipedia that arise due to the 2005 government censor- ship in mainland China. Using the exogenous variation in the fraction of editors blocked across different articles due to the censorship, we examine the impact of reduction in group size, which we denote as the shock level, on three collaborative behavior measures: volume of activity, centralization, and conflict. We find that activity and conflict drop on articles that face a shock, whereas centralization increases. The impact of a shock on activity increases with shock level, whereas the impact on centralization and conflict is higher for moderate shock levels than for very small or very high shock levels. These findings provide support for threat rigidity theory -- originally introduced in the organizational theory literature -- in the context of large-scale collaborative crowds

HotRAP: Hot Record Retention and Promotion for LSM-trees with tiered storage

The multi-level design of Log-Structured Merge-trees (LSM-trees) naturally fits the tiered storage architecture: the upper levels (recently inserted/updated records) are kept in fast storage to guarantee performance while the lower levels (the majority of records) are placed in slower but cheaper storage to reduce cost. However, frequently accessed records may have been compacted and reside in slow storage, and existing algorithms are inefficient in promoting these ``hot'' records to fast storage, leading to compromised read performance. We present HotRAP, a key-value store based on RocksDB that can timely promote hot records individually from slow to fast storage and keep them in fast storage while they are hot. HotRAP uses an on-disk data structure (a specially-made LSM-tree) to track the hotness of keys and includes three pathways to ensure that hot records reach fast storage with short delays. Our experiments show that HotRAP outperforms state-of-the-art LSM-trees on tiered storage by up to 3.3$\times$ compared to the second best for read-only and read-write-balanced workloads with common access skew patterns

Eventful migration: Rethinking social media migration with help from Elon Musk’s sink

Using the 2022 Twitter to Mastodon migration as a case study, this article contributes a new understanding of social media migration (SMM). It begins with a review of existing studies of SMM, showing how migration is often understood as a combination of ‘push and pull factors’. We suggest a need to widen the conceptual scope for how we approach SMM in ways that more directly tie such movements to specific questions of power, agency and events that ripple through digital cultures. Drawing on social media account analysis, a survey of recently migrated Mastodon users, content from high-profile Twitter users and other media commentaries, we re-present the migration in order to detail our ‘eventful’ theory of migration. Eventful social media migration is comprised of five elements: an initial X factor; the emergence of a critical voice; a collective platform consciousness; an observable migration; and a wider terrain transformation

Customer Profiling Based on Mobile Apps GPS Data : A Case Study on Westfield Shopping Malls

In order to provide a personalised experience to customers, it’s essential for shopping centers to understand its customer base and their shopping behaviors. Building a well-developed customer profile is critical for improving marketing efficiency, expanding market share, and building long-term, stable business ties with trading partners. Currently most shopping malls or retail business use footfall or customer surveys to grasp the customer behaviors, which are insufficient to obtain accurate and representative information about the customers. This study aims to provide a detailed customer profile for shopping centers using GPS datasets. We choose the two Westfield shopping malls in London as the case study area. In order to uncover additional customer information, this study focuses four research questions:(1) Origin places of customers; (2) Their transportation mode to the mall; (3) The average dwell time of customers; (4) The pattern of return visitors. According to the results, malls can develop a range of marketing initiatives to provide a better shopping experience for customers and attract more of them

Understanding Hyperbolic Metric Learning through Hard Negative Sampling

In recent years, there has been a growing trend of incorporating hyperbolic geometry methods into computer vision. While these methods have achieved state-of-the-art performance on various metric learning tasks using hyperbolic distance measurements, the underlying theoretical analysis supporting this superior performance remains under-exploited. In this study, we investigate the effects of integrating hyperbolic space into metric learning, particularly when training with contrastive loss. We identify a need for a comprehensive comparison between Euclidean and hyperbolic spaces regarding the temperature effect in the contrastive loss within the existing literature. To address this gap, we conduct an extensive investigation to benchmark the results of Vision Transformers (ViTs) using a hybrid objective function that combines loss from Euclidean and hyperbolic spaces. Additionally, we provide a theoretical analysis of the observed performance improvement. We also reveal that hyperbolic metric learning is highly related to hard negative sampling, providing insights for future work. This work will provide valuable data points and experience in understanding hyperbolic image embeddings. To shed more light on problem-solving and encourage further investigation into our approach, our code is available online (https://github.com/YunYunY/HypMix).Comment: published in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 202