Detecting social bots is critical to ensuring the security of online discourse and maintaining trust in social networks. Early feature-based and text-based methods often fail against bots that mimic human behavior, and graph-based approaches have emerged to better exploit structural signals. However, most existing Graph Neural Networks (GNNs) still focus on pairwise connections, overlooking higher-order relational patterns, and their multi-relation fusion strategies are typically simplistic, ignoring dependencies between relations and user-specific preferences. To overcome these limitations, we propose MPS-Bot, a model that integrates higher-order structure modeling with user-specific cross-relation dependency learning. MPS-Bot introduces a simplex convolutional layer that leverages simplexes derived from network structures to capture group coordination patterns beyond pairwise connections. In addition, a cross-relation dependency attention mechanism adaptively fuses relation-specific representations according to each user's relational preferences, leading to more discriminative and robust multi-relation representations. Extensive experiments on two widely used Twitter bot detection benchmarks, MGTAB and TwiBot-22, show that MPS-Bot generally outperforms state-of-the-art baselines. These findings highlight the effectiveness of higher-dimensional message passing over simplexes as a powerful approach to unmasking bots in social networks.

Instant delivery has become an essential service in daily life, requiring strict delivery timelines. However, traditional delivery methods that employ human couriers struggle to meet the soaring delivery demands due to labor shortages. While researchers have explored alternative solutions using ground vehicles (e.g., crowdsourced taxis) and Unmanned Aerial Vehicles (UAVs), their inherent limitations, such as constrained delivery detour for crowdsourced taxis and limited battery capacity of UAVs, greatly constrain their effectiveness. To address these challenges, this paper proposes a novel air-ground delivery paradigm that cooperatively integrates UAVs and crowdsourced taxis. First, UAV stations are strategically deployed based on delivery gaps between the delivery demands and taxis' delivery capacity, instead of delivery demands only; Then, a predictive UAV repositioning strategy is designed to bridge instantaneously dynamic delivery gaps. Thereafter, a transfer learning-based (TL-based) algorithm that mines the delivery knowledge of human couriers is designed to optimize the cooperative performance. This algorithm extracts behavioral insights from human couriers and transfers them to enhance the delivery capabilities of UAVs and taxis. Finally, parcel assignment is formulated as optimization problems aimed at maximizing total preferences of UAVs and taxis, and maximizing delivery number while minimizing cost, respectively. Evaluations on real-world datasets demonstrate that the proposed method delivers 27.4% more parcels, saves 19.2% delivery cost, and preserves 36.3% more of the travel experience of taxi passengers than the state-of-the-art (SOTA) air-ground cooperative approach for instant delivery.

In the contemporary era, characterized by an accelerated development in the domain of social networks, the phenomenon of fake news has attained unprecedented levels of prevalence, exerting substantial detrimental influence on society. Identification of the sources of such information in a timely manner is of paramount importance in order to prevent further damage. Existing source localization methods can be categorized into two distinct approaches: the first involves the deployment of observers followed by localization, while the second employs traditional community partitioning for source localization without considering community structure in observer deployment, resulting in suboptimal information acquisition. To address this issue, we propose Community Partition-Based Source Localization with Adaptive Observers Deployment (CSOL), which consists of three stages: In the first stage, community partitioning is achieved using contrastive learning with optimization and a feature extraction module that is highly correlated with partition. In the second stage, we are the first work to adaptively deploy observer based on community importance, integrating community partitioning with observer placement. In the third stage, an early source estimation strategy is employed to enhance efficiency and accuracy. Experimental results in real-world networks demonstrate that CSOL outperforms other SOTA methods in both accuracy and efficiency.

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery.

Electric vehicles (EVs), which have become one of the most important commuting vehicles in the world, heavily depend on the support of robust and efficient grids. Many researchers focus on optimizing the charging of EVs by the grid. However, they overlook the emergency charging when the large-scale grid fails. Inspired by the DC-2-DC technology, EVs can achieve vehicle-2-vehicle (V2V) energy sharing between each other, which inspires us to study leveraging the significant number of electric buses (e-buses) as effective and cost-efficient standby energy to charge low-power EVs when a large-scale grid outage occurs, whereas the feasibility, scheduling algorithm, and performance of e-bus-based V2V energy sharing remain open questions. With this in mind, we first reveal that it is feasible for e-buses to serve as effective standby power for low-power EVs, after careful analysis of real-world data. Then, we conduct a data-driven estimation of the energy consumption to determine the volume of the V2V shared energy for large-scale EVs. Accordingly, we formulate the e-bus-based V2V energy sharing problem, whose two objectives are to maximize the total number of the sufficiently charged EVs and to minimize the distance of these EVs moving to the e-bus for charging. The first objective is to charge more low-power EVs with the limited energy volume of the e-buses and the second objective is to reduce the range anxiety of the low-power EV drivers. Observing that existing algorithm is time-consuming or inefficient, which cannot be applied for the problem instance with a large number of e-buses and EVs, we design a low-complexity approximation algorithm for the problem. Both theoretical analysis and comprehensive data-driven evaluation demonstrate that the proposed algorithm, 1) significantly optimizes the optimization objectives by 23.6% and 15.8% on average, and up to 34.8% and 34.0% in extreme cases, compared with the existing approximation algorithm and 2) achieves over$100\times $execution speed improvement compared to the linear programming (LP)-based algorithm and evolutionary learning (EL) algorithms. The results demonstrate that the proposed algorithm achieves a better overall performance and makes it a promising solution for large-scale problem instances.

Unmanned Aerial Vehicles (UAVs) are widely applied in smart city applications such as urban sensing and delivery, due to the UAVs’ agility, low cost and not being restricted by ground road conditions. However, the limited battery capacity becomes one of the biggest obstacles to the application of UAVs. To address this issue, this paper investigates an emergency response application, in which UAVs generally ride crowdsourced buses to save energy and respond to a stochastic emergency event (such as a traffic accident) when the event occurs. For the bus-based UAV response paradigm, a single UAV response process with the constraint of the bus mobility is first modeled. Subsequently, a data-driven UAV path planning algorithm is designed. Then two emergency response cases by multi-UAV are investigated. One case is irregular emergency response, whose objective is to maximize the temporal-spatial coverage of the urban area. The other case is predictable emergency response, which optimizes the response performance to these emergencies. Thereafter, the bus-stimulating problems for the two cases are formulated and solved. Finally, utilizing a real-world bus trajectory dataset generated by a large-scale bus fleet and a traffic event dataset, the emergency response performance of the bus-based UAV response paradigm is comprehensively evaluated. The results show that (1) with only 30 UAVs, 90% of Shenzhen city can be covered in the irregular emergency response case; (2) with only 50 UAVs, the average response delay to the emergencies is shorter than 1.5 minutes, which is 56% shorter than baselines, in the predictable emergencies response case.

Large-scale social events like civil unrest, distinctly impacts our daily life. For this reason, it is essential to predict specific events in advance based on relevant information. Studies on data-driven event prediction assume that there are precursors for predictable events that can be tracked in history. Therefore, modeling prior evolution of a target event appropriately using relevant information or implicit indicators is of great importance to anticipate whether concerned events are likely to occur sometime in the future. However, there are issues among existing relevant studies: (I) how to properly define event data for training to match the realistic prediction scenario. (II) how to extract useful previous information from available data flow and model relevant evolution or dynamic feature of events reasonably. (III) it is both practical and urgent to mine precursors or clues from spatial-temporal data for interpreting prediction results. In this paper, we propose a novel feature learning framework for event prediction that can discover potential precursors from the input data. The prediction model primarily consists of a graph encoder module using GNN (Graph Neural Network) techniques and a temporal feature learning module employing attention mechanism. Meanwhile, we develop a backward tracking method to model the previous evolution of an event by retrieving prospective relevant events in the past. Multiple experiments conducted on datasets collected from various regions in the real world demonstrate appreciable performance of our proposed model in social event prediction task.

Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes.We demonstrate wide performance gaps across demographic groups and show that pretrained language models systematically disfavor young non-white male speakers; i.e., not only do pretrained language models learn social biases (stereotypical associations) -pretrained language models also learn sociolectal biases, learning to speak more like some than like others.We show, however, that, with the exception of BERT models, larger pretrained language models reduce some the performance gaps between majority and minority groups.

Aspect-level sentiment classification is a fine-grained sentiment analysis task, which aims to predict the sentiment of a text in different aspects. One key point of this task is to allocate the appropriate sentiment words for the given aspect.Recent work exploits attention neural networks to allocate sentiment words and achieves the state-of-the-art performance. However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect. To solve this problem, we propose a HiErarchical ATtention (HEAT) network for aspect-level sentiment classification. The HEAT network contains a hierarchical attention module, consisting of aspect attention and sentiment attention. The aspect attention extracts the aspect-related information to guide the sentiment attention to better allocate aspect-specific sentiment words of the text. Moreover, the HEAT network supports to extract the aspect terms together with aspect-level sentiment classification by introducing the Bernoulli attention mechanism. To verify the proposed method, we conduct experiments on restaurant and laptop review data sets from SemEval at both the sentence level and the review level. The experimental results show that our model better allocates appropriate sentiment expressions for a given aspect benefiting from the guidance of aspect terms. Moreover, our method achieves better performance on aspect-level sentiment classification than state-of-the-art models.

Social multimedia content had an unprecedented increasing trend in recent years, and receiving a number of research attentions. Images, an exceedingly expressive form of social multimedia, can be widely seen in news report for social emergency. Among the vast number of images for social emergency are many repurposed images, that is, variants not interpreted as what the original images express. Such repurposed images appear in many online pages and may mislead the public. This make it being an interesting and challenging task to identify whether an image is repurposed. We propose a novel framework, called SOFC, to identify the repurposed images. A SIFT-based identical parts finding algorithm is used to find and align all potential identical blocks in the repurposed images and the original images. We then compute the similarity of the potential identical blocks by implementing an object-based likelihood measuring algorithm, to determine whether these blocks are identical in the two images. Finally, the effectiveness of the proposed identification method is validated by experiments on a image set of real social emergency.

This paper proposes a new texture description metric, the color distribution evenness (CDE) measure, and discusses its usage of performing multi-scale texture analysis. Further, CDE measure is applied to color-texture segmentation of natural images, upon which we propose the ISBEC (Image Segmentation Based on the distribution Evenness of Colors) algorithm. Experiments verify the effectiveness of CDE measure for texture analysis and that of ISBEC for color-texture segmentation.