面向社交媒体的直接因果网络发现算法

高维时序因果网络发现是社交媒体因果关系发现的重要问题。然而,现有的时序因果关系发现方法不能发现直接因果以致因果网络推断结果不准确。针对此问题提出了一种直接因果网络发现方法。该方法考虑了时序因果模型的因果延迟、滞后期数量和条件节点集等因素,更准确地发现直接因果关系;另外,采用结合置换检验的因果关系检验方法,解决传递熵阈值难以设定的问题。实验结果表明,该方法在因果网络推断中优于现有方法,有效提升时序上直接因果网络推断的准确率,适用于发现潜在社交媒体因果关系网络。     

Geometric discriminative features for aerial image retrieval in social media

The aerial image recognition is an important problem in multimedia information retrieval in social media. In this paper, we propose a new approach by integrating aerial image’s local features into a discriminative one which reflects both the geometric property and the color distribution of aerial image. Firstly, each aerial image is segmented into several regions in terms of their color intensities. And region connected graph (RCG), the links between the spatial neighboring regions, is presented to encode the spatial context of aerial images. Secondly, we mine frequent structures in the RCGs corresponding to training aerial images collected from social media. And a set of refined structures are selected among the frequent ones towards being more discriminative and less redundant. Finally, given a new aerial image, its sub-RCGs corresponding to all the refined structures are extracted and quantized into a discriminative feature for aerial image recognition. The experimental results validate the proposed method by providing a more accurate recognition result of the aerial images on different datasets from different social medias.     

Generating and exploiting customer insights from social media data

Previous research has emphasized the virtues of customer insights as a key source of competitive advantage. The rise of customers’ social media use allows firms to collect customer data in an ever-increasing volume and variety. However, to date, little is known about the capabilities required of firms to turn social media data into valuable customer insights and exploit these insights to create added value for customers. Based on the dynamic capabilities perspective, in particular the concept of absorptive capacity (ACAP), the authors conducted multiple case studies of seven mid-sized and large B2C firms in Switzerland and Germany. The results provide an in-depth analysis of the underlying processes of ACAP as well as contingent factors – that is, physical, human and organizational resources that underpin the firms’ ACAP.     

基于流形学习的社会化媒体网络数据分类

社会化媒体提供了海量的、大尺度的异质网络数据,如何对网络数据进行分类是一个亟待解决的新问题。基于潜在社会维模型,提出利用流形学习中的拉普拉斯特征映射算法进行社会维抽取。实验表明,在精确匹配率、微平均、宏平均等性能指标上,均优于基于模块度最大化的原有社会维模型。该算法能更好地获取用户的隐性联系,从而更好地分析网络用户行为。     

Extracting interrelated information from road-related social media data

The social media data (SMD) have been viewed as a potential and promising information source of road conditions. However, most existing SMD-based sensing approaches (SMDSAs) either ignore interrelations among information items (e.g., name, direction, and status of the road) or rely on rigid grammar rules to establish entities’ interrelations. Additionally, current SMDSAs in the transportation domain are unable to link the extracted text-formatted information with domain-specific models (e.g., virtual road model, VRM). In order to fill such gaps, this work proposes an improved SMDSA of road conditions, which involves a three-stage (i.e., SMD classification, relation inference, and entity pair recognition) interrelated information extraction model, as well as a semantic converter to feed the SMD-provided text-formatted information into VRMs. The proposed SMDSA is demonstrated by the newly annotated datasets of tweets in Lexington, USA. The three-stage interrelated information extraction model outperforms conventional rule-based methods and deep-learning algorithms (e.g., Text CNN, Bi-LSTM, Piecewise CNN, and Capsule Net). The SMD-enabled VRM also preliminarily shows its capacity to optimize signal timings during incidents that change the road network topology. This work contributes to circumventing the reliance on human-made rules during SMDSAs’ development, bridging user-generated SMD with operable VRMs for potential real-world road management, and providing a standard tweet dataset annotated with interrelation triplets to help promote SMDSA studies.     

Logo information recognition in large-scale social media data

Recent years have shown us the quick development of social network. For companies, microblog platform is more and more important as one source to disseminate brand information and monitor their development. Compared with the frequently used text information existing in traditional media, microblog platform provides information about brands in more types such as images and other related information forms. According to the statistics, microblogs posted on social network contain more and more percentage of images. Hence how to recognize logos in images from social network is of high value. To address this problem, we propose a novel learning-based logo detection method with social network information assistance. A new dense histogram type feature is proposed to classify logo and non-logo image patches. To increase the detection precision, social network content is analyzed and employed to do filtering to reduce detection window candidates. Through the evaluation on large-scale data collected from Sina Weibo platform, the proposed method is demonstrated effective.     

Social velocity based spatio-temporal anomalous daily activity discovery of social media users

Applied Intelligence - Anomalous daily activities are the activities that do not fit into normal daily behavior of social media users. Discovering anomalous daily activities is important for...     

Feature selection for event discovery in social media: A comparative study

Microblog as one kind of typical social media has many research implications in social event discovery and social-media-based e-learning and collaborative learning. At present, researchers usually employ feature-based classification approaches to detect social events in microblogs. However, it is very common to get different results when different features are used in event discovery. Therefore, it has been a critical issue how to select appropriate features for event discovery in microblogs. In this paper, we analyze five different feature selection methods and present an improved method for selecting features for microblog-based event discovery. We compare all the methods on a real microblog dataset in terms of various metrics including precision, recall, and F-measure. And finally we discuss the best feature selection method for the event discovery in microblogs. To the best of our knowledge, there are no such comparative studies on feature selection for event discovery in social media, and this paper is expected to offer some useful references for the future research and applications on the event discovery in microblogs.     

A taxonomy and survey of big data in social media

Examining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high-quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data-trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1-score, and 5% run time complexity.     

Recognizing composite daily activities from crowd-labelled social media data

Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analysing signals of wearable motion sensors. While successful for low-level activities (e.g. walking or standing), high-level activities (e.g. watching movies or attending lectures) are difficult to distinguish from motion data alone. Furthermore, instrumentation of complex body sensor network at population scale is impractical. In this work, we take an alternative approach of leveraging rich, dynamic, and crowd-generated self-report data from social media platforms as the basis for in-situ activity recognition. By treating the user as the “sensor”, we make use of implicit signals emitted from natural use of mobile smartphones, in the form of textual content, semantic location, and time. Tackling both the task of recognizing a main activity (multi-class classification) and recognizing all applicable activity categories (multi-label tagging) from one instance, we are able to obtain mean accuracies of more than 75%. We conduct a thorough analysis and interpret of our model to illustrate a promising first step towards comprehensive, high-level activity recognition using instrumentation-free, crowdsourced, social media data.     

CrimeTelescope: crime hotspot prediction based on urban and social media data fusion

Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot prediction leverages past data in order to identify geographical areas susceptible of hosting crimes in the future. However, most of the existing techniques in crime hotspot prediction solely use historical crime records to identify crime hotspots, while ignoring the predictive power of other data such as urban or social media data. In this paper, we propose CrimeTelescope, a platform that predicts and visualizes crime hotspots based on a fusion of different data types. Our platform continuously collects crime data as well as urban and social media data on the Web. It then extracts key features from the collected data based on both statistical and linguistic analysis. Finally, it identifies crime hotspots by leveraging the extracted features, and offers visualizations of the hotspots on an interactive map. Based on real-world data collected from New York City, we show that combining different types of data can effectively improve the crime hotspot prediction accuracy (by up to 5.2%), compared to classical approaches based on historical crime records only. In addition, we demonstrate the usability of our platform through a System Usability Scale (SUS) survey on a full prototype of CrimeTelescope.     

Crowdsourcing based social media data analysis of urban emergency events

Multimedia Tools and Applications - An urban emergency event requires an immediate reaction or assistance for an emergency situation. With the popularity of the World Wide Web, the internet is...     

Short-text feature construction and selection in social media data: a survey

Social networking sites such as Facebook or Twitter attract millions of users, who everyday post an enormous amount of content in the form of tweets, comments and posts. Since social network texts are usually short, learning tasks have to deal with a very high dimensional and sparse feature space, in which most features have low frequencies. As a result, extracting useful knowledge from such noisy data is a challenging task, that converts large-scale short-text learning tasks in social environments into one of the most relevant problems in machine learning and data mining. Feature selection is one of the most known and commonly used techniques for reducing the impact of the high dimensional feature space in text learning. A wide variety of feature selection techniques can be found in the literature applied to traditional, long-texts and document collections. However, short-texts coming from the social Web pose new challenges to this well-studied problem as texts’ shortness offers a limited context to extract enough statistical evidence about words relations (e.g. correlation), and instances usually arrive in continuous streams (e.g. Twitter timeline), so that the number of features and instances is unknown, among other problems. This paper surveys feature selection techniques for dealing with short texts in both offline and online settings. Then, open issues and research opportunities for performing online feature selection over social media data are discussed.     

Enterprise reputation threats on social media: A case of data breach framing

Reputation threats on social media in the aftermath of a data breach is a critical concern to enterprises. We argue that any effort to minimize reputation threats will require an orderly assessment of how reputation threat manifests on social media. Drawing on crisis communication and social media literature, we analyze Twitter postings related to the 2014 Home Depot data breach. We identify a taxonomy of data breach frames and sub-frames and the related reputation threats as manifested by data breach responsibility-attributions and negative emotional responses. Results indicate that reputation threats vary for intentional, accidental, and victim data breach frames. Based on crisis stage theory, we also analyze the dynamics of evolving reputation threats as data breach situation unfolds on social media. Results suggest that the data breach frames and associated reputation threats vary across the crisis stages. Further, intentional and accidental frames increase subsequent responsibility-attributions and negative emotions. Tweets with responsibility-attributions further increase the subsequent generation of reputation-threatening tweets. Negative emotions, particularly anger and disgust, also increase subsequent reputation threats. Our study has implications for enterprise reputation management and word-of-mouth literature. The results yield valuable insights that can guide enterprise strategy for social media reputation management and post data breach intervention.     

Quantify city-level dynamic functions across China using social media and POIs data

Location-aware big data from social media have been widely used to study functions of different zones in a city but not across a city as a whole. In this study, a novel framework is proposed to quantify city-level dynamic functions of 200 cities in China from a perspective of collective human activities. The random forest model was used to determine the temporal variations in the proportions of different urban functions by examining the relationship between Points-of-Interest (POIs) and Tencent Location Request (TLR) data. We then hierarchically clustered and analyzed the structures and distribution patterns of the dynamic urban functions of 200 Chinese cities at different temporal scales. In the end, we calculated an urban functional equilibrium index based on the urban functional proportion and then mapped spatial distribution patterns of the indexes across mainland China. Results show that on a daily scale when the cities were grouped into two clusters, they are either dominated by the work/education and commerce or residence functions. The cities in the former cluster are mainly the provincial capitals and located within major urban agglomerations. When the cities were grouped into four clusters, the clusters are dominated their commerce, work, residence, and balanced multiple functions, respectively. For each of the 200 cities, its urban functions change dynamically from the daybreak to the evening in terms of human activities. Besides, the equilibrium indexes show a power-law relationship with their rankings. Our research shows that city-level dynamic function can be quantified from the perspective of variations in human activities by using social media big data that otherwise could not be achieved in the conventional urban functions’ studies.