Multimodal Classification of Urban Micro-Events

In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these are difficult to identify in most data sources. However, by using citizen sensing to gather data, detecting them becomes feasible. The data gathered by citizen sensing is often multimodal and, as a consequence, the information required to detect urban micro-events is distributed over multiple modalities. This makes it essential to have a classifier capable of combining them. In this paper we explore several methods of creating such a classifier, including early, late, hybrid fusion and representation learning using multimodal graphs. We evaluate performance on a real world dataset obtained from a live citizen reporting system. We show that a multimodal approach yields higher performance than unimodal alternatives. Furthermore, we demonstrate that our hybrid combination of early and late fusion with multimodal embeddings performs best in classification of urban micro-events

Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions. Our codes are available at https://github.com/MengzSun/KDCN

A study on Analysis and Utilization of Crowd-sourced Spatio-temporal Contexts from Social Media

兵庫県立大学大学院201

Social Media as a Medium to Promote Local Perception Expression in China’s World Heritage Sites

The assessment of public participation is one of the most fundamental components of holistic and sustainable cultural heritage management. Since the beginning of 2020, the COVID-19 pandemic became a catalyst for the transformation of participatory tools. Collaboration with stakeholders moved online due to the strict restrictions preventing on-site activities. This phenomenon provided an opportunity to formulate more comprehensive and reasonable urban heritage protection strategies. However, very few publications mentioned how social networking sites’ data could support humanity-centred heritage management and participatory evaluation. Taking five World Cultural Heritage Sites as research samples, the study provides a methodology to evaluate online participatory practices in China through Weibo, a Chinese-originated social media platform. The data obtained were analysed from three perspectives: the users’ information, the content of texts, and the attached images. As shown in the results section, individuals’ information is described by gender, geo-location, celebrities, and Key Opinion Leaders. To a greater extent, participatory behaviour emerges at the relatively primary levels, that being “informing and consulting”. According to the label detection of Google Vision, residents paid more attention to buildings, facades, and temples in the cultural heritage sites. The research concludes that using social media platforms to unveil interplays between digital and physical heritage conservation is feasible and should be widely encouraged

Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201