Characteristics Of Social Media Data Research Articles

Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial–temporal–semantic features

AbstractStudying human behavioral patterns from social media data is an important part of emergency management. However, the multidimensional characteristics of social media data have rarely been fully utilized. This study proposes a multidimensional analytical framework for social media user behavior that integrates time–geographic–semantic features. The framework defines the spatiotemporal semantic multidimensional relationship of social media user behavior and maps it into a time–geographic–semantic (TGS) cube, based on which a TGS‐weighted similarity measure was created. We then applied a spectral clustering algorithm to cluster the trajectories of the user behavior. Subsequently, a prefix‐projected pattern growth algorithm was used to mine frequent semantic patterns from the clustering results and analyze their spatiotemporal distribution characteristics. Taking the COVID‐19 pandemic as a case study, we analyzed Weibo user behavior in China from January 9 to March 10, 2020. The results showed that the clustering of TGS similarity was better than that of the commonly used edit distance on real and longest common subsequences. Five semantic patterns of public responses were identified during the COVID‐19 pandemic. The semantic patterns of categories 1, 2, 4, and 5 were “spindle‐shaped,” meaning that their core semantics were stable and concentrated on one or several topics despite the frequent semantic changes in the middle stage. Category 3 was “wave‐shaped,” indicating that their semantics fluctuated between serval topics during the pandemic. This discovery shows that the framework is suitable for analyzing and comprehensively understanding public behavior during pandemic emergencies. This framework has good universality and great potential for extension to other emergencies.

Application and Analysis of Social Media Data in Economic Research

As Internet technology enters a new era, social media has become an essential platform for people to obtain information and express their views, which has profoundly impacted economic research. This paper focuses on the application of social media data in economic research. First, this paper analyzes the essential performance and characteristics of social media data, such as user engagement, the speed of information diffusion, and content diversity. Second, it explores the expression of emotions on social media, including immediate reactions to economic events, analysis of the acceptability of economic policies, and the social psychology of economic issues. In addition, we analyze the reasons why social media data affects the economy, including the recommendation mechanism of algorithms, changes in social phenomena due to economic globalization, economic inequality and differentiation of public opinion among social classes, and distortion of economic opinions caused by information bubbles. Finally, we propose strategies to prevent social media data bias, including data cleaning and analysis, multi-dimensional information integration strategies, laws, regulations and technical measures, and education guidance. This paper aims to provide a comprehensive framework for analyzing social media data for economic research so as to promote the high- quality development of economic research.

Exploring the evolutionary characteristics of social media data in metro emergencies: A case study of Zhengzhou Metro flood

Exploring the evolutionary characteristics of social media data in metro emergencies: A case study of Zhengzhou Metro flood

Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data.

The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey‐related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no‐sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey‐derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco‐health achievement.

Harnessing social media to understand tourist mobility: the role of information technology and big data

Uso de las redes sociales para comprender la movilidad turística: el papel de la tecnología de la información y los macrodatosResumenObjetivoEn este estudio se examinan los parámetros clave en el contexto académico y enfoque geográfico y se evalúan los fundamentos teóricos de estudios en el campo de las redes sociales y la movilidad de los visitantes. Se resumen también las características de datos de las redes sociales, incluidos los métodos de recopilación de datos de las diferentes plataformas de redes sociales así como sus ventajas y limitaciones. Finalmente, se examinan tecnologías y métodos aplicados que respaldan las diferentes cuestiones de la investigación.ResultadosEl estudio proporciona una evaluación avanzada del conocimiento hasta la fecha sobre la movilidad turística analizada utilizando datos de redes sociales. La diversidad de escalas (con un enfoque dominante en la escala de la ciudad), plataformas y métodos indica que este campo está en auge, pero también refleja la complejidad del fenómeno turístico. En este estudio se identifica una falta de teoría en este campo y se señalan los contúnios desafíos para garantizar el uso apropiado de datos (por ejemplo, diferenciar a los viajeros de los residentes) y la ética que los rodea.Originalidad / valorlos resultados guían a los investigadores, especialmente a aquellos sin formación en informática, sobre los diferentes tipos de enfoques, fuentes de datos y métodos disponibles para rastrear la movilidad turística mediante el uso de las redes sociales. Existen diferentes herramientas de procesamiento y visualización disponibles dependiendo del interés particular de la investigación.Palabras clave:Patrones de viaje; Movilidad turística; Movimientos de visitantes; Redes sociales; Tecnologías de la información; Macrodatos.

Research on event perception based on geo-tagged social media data

Abstract. Technological advancement makes information dissemination more convenient. When a notable event occurs, social media serves a popular platform for citizens to share event-related information. Therefore, in the information age, how to effectively observe the event and improve event management ability is an open question worthy of attention. Traditional social survey methods and various automatic sensors have been widely used to monitor the specific event. However, widely used social media service provides a unique approach for the event study with individuals as smart sensors. How to perceive an event through social media data has triggered a series of researches. Currently, we can find when, where what happened and induced impact based on geo-tagged social media data. However, event study based on social media is still in its infancy. This paper provides an overview of event study based on geo-tagged social media data. Firstly, we introduce the event model and the characteristics of social media data. Then, how to detect and trace event, how to analyze event impact and visually express obtained knowledge are discussed respectively. Subsequently, based on the existing researches, we propose further questions and conclude.

SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media.

With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., drug indication and beneficial effect) could hold between the drug and adverse event mentions, making ADE relation extraction - distinguishing ADE relationship from other relation types - necessary. However, conducting ADE relation extraction in social media environment is not a trivial task because of the expertise-dependent, time-consuming and costly annotation process, and the feature space's high-dimensionality attributed to intrinsic characteristics of social media data. This study aims to develop a framework for ADE relation extraction using patient-generated content in social media with better performance than that delivered by previous efforts. To achieve the objective, a general semi-supervised ensemble learning framework, SSEL-ADE, was developed. The framework exploited various lexical, semantic, and syntactic features, and integrated ensemble learning and semi-supervised learning. A series of experiments were conducted to verify the effectiveness of the proposed framework. Empirical results demonstrate the effectiveness of each component of SSEL-ADE and reveal that our proposed framework outperforms most of existing ADE relation extraction methods The SSEL-ADE can facilitate enhanced ADE relation extraction performance, thereby providing more reliable support for pharmacovigilance. Moreover, the proposed semi-supervised ensemble methods have the potential of being applied to effectively deal with other social media-based problems.

Real-Time Multimedia Social Event Detection in Microblog.

Detecting events from massive social media data in social networks can facilitate browsing, search, and monitoring of real-time events by corporations, governments, and users. The short, conversational, heterogeneous, and real-time characteristics of social media data bring great challenges for event detection. The existing event detection approaches rely mainly on textual information, while the visual content of microblogs and the intrinsic correlation among the heterogeneous data are scarcely explored. To deal with the above challenges, we propose a novel real-time event detection method by generating an intermediate semantic level from social multimedia data, named microblog clique (MC), which is able to explore the high correlations among different microblogs. Specifically, the proposed method comprises three stages. First, the heterogeneous data in microblogs is formulated in a hypergraph structure. Hypergraph cut is conducted to group the highly correlated microblogs with the same topics as the MCs, which can address the information inadequateness and data sparseness issues. Second, a bipartite graph is constructed based on the generated MCs and the transfer cut partition is performed to detect the events. Finally, for new incoming microblogs, incremental hypergraph is constructed based on the latest MCs to generate new MCs, which are classified by bipartite graph partition into existing events or new ones. Extensive experiments are conducted on the events in the Brand-Social-Net dataset and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.

Discovering, assessing, and mitigating data bias in social media

Social media has generated a wealth of data. Billions of people tweet, sharing, post, and discuss everyday. Due to this increased activity, social media platforms provide new opportunities for research about human behavior, information diffusion, and influence propagation at a scale that is otherwise impossible. Social media data is a new treasure trove for data mining and predictive analytics. Since social media data differs from conventional data, it is imperative to study its unique characteristics. This work investigates data collection bias associated with social media. In particular, we propose computational methods to assess if there is bias due to the way a social media site makes its data available, to detect bias from data samples without access to the full data, and to mitigate bias by designing data collection strategies that maximize coverage to minimize bias. We also present a new kind of data bias stemming from API attacks with both algorithms, data, and validation results. This work demonstrates how some characteristics of social media data can be extensively studied and verified and how corresponding intervention mechanisms can be designed to overcome negative effects. The methods and findings of this work could be helpful in studying different characteristics of social media data.

Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis.

Twitter and related social media feeds have become valuable data sources to many fields of research. Numerous researchers have thereby used social media posts for spatial analysis, since many of them contain explicit geographic locations. However, despite its widespread use within applied research, a thorough understanding of the underlying spatial characteristics of these data is still lacking. In this paper, we investigate how topological outliers influence the outcomes of spatial analyses of social media data. These outliers appear when different users contribute heterogeneous information about different phenomena simultaneously from similar locations. As a consequence, various messages representing different spatial phenomena are captured closely to each other, and are at risk to be falsely related in a spatial analysis. Our results reveal indications for corresponding spurious effects when analyzing Twitter data. Further, we show how the outliers distort the range of outcomes of spatial analysis methods. This has significant influence on the power of spatial inferential techniques, and, more generally, on the validity and interpretability of spatial analysis results. We further investigate how the issues caused by topological outliers are composed in detail. We unveil that multiple disturbing effects are acting simultaneously and that these are related to the geographic scales of the involved overlapping patterns. Our results show that at some scale configurations, the disturbances added through overlap are more severe than at others. Further, their behavior turns into a volatile and almost chaotic fluctuation when the scales of the involved patterns become too different. Overall, our results highlight the critical importance of thoroughly considering the specific characteristics of social media data when analyzing them spatially.

A Mixed‐Method Approach to Extracting the Value of Social Media Data

In the last decade, social media platforms have become important communication channels between businesses and consumers. As a result, a lot of consumer‐generated data are available online. Unfortunately, they are not fully utilized, partly because of their nature: they are unstructured, subjective, and exist in massive databases. To make use of these data, more than one research method is needed. This study proposes a new, multiple approach to social media data analysis, which counteracts the aforementioned characteristics of social media data. In this new approach the data are first extracted systematically and coded following the principles of content analysis, after a comprehensive literature review has been conducted to guide the coding strategy. Next, the relationships between codes are identified by statistical cluster analysis. These relationships are used in the next step of the analysis, where evaluation criteria weights are derived on the basis of the social media data through probability weighting function. A case study is employed to test the proposed approach.

An Unsupervised Feature Selection Framework for Social Media Data

The explosive usage of social media produces massive amount of unlabeled and high-dimensional data. Feature selection has been proven to be effective in dealing with high-dimensional data for efficient learning and data mining. Unsupervised feature selection remains a challenging task due to the absence of label information based on which feature relevance is often assessed. The unique characteristics of social media data further complicate the already challenging problem of unsupervised feature selection, e.g., social media data is inherently linked, which makes invalid the independent and identically distributed assumption, bringing about new challenges to unsupervised feature selection algorithms. In this paper, we investigate a novel problem of feature selection for social media data in an unsupervised scenario. In particular, we analyze the differences between social media data and traditional attribute-value data, investigate how the relations extracted from linked data can be exploited to help select relevant features, and propose a novel unsupervised feature selection framework, LUFS, for linked social media data. We systematically design and conduct systemic experiments to evaluate the proposed framework on data sets from real-world social media websites. The empirical study demonstrates the effectiveness and potential of our proposed framework.

A Unified Framework of Latent Feature <newline/>Learning in Social Media

The current trend in social media analysis and application is to use the pre-defined features and devoted to the later model development modules to meet the end tasks. Representation learning has been a fundamental problem in machine learning, and widely recognized as critical to the performance of end tasks. In this paper, we provide evidence that specially learned features will addresses the diverse, heterogeneous, and collective characteristics of social media data. Therefore, we propose to transfer the focus from the model development to latent feature learning, and present a unified framework of latent feature learning on social media. To address the noisy, diverse, heterogeneous, and interconnected characteristics of social media data, the popular deep learning is employed due to its excellent abstract abilities. In particular, we instantiate the proposed framework by (1) designing a novel relational generative deep learning model to solve the social media link analysis task, and (2) developing a multimodal deep learning to lambda rank model towards the social image retrieval task. We show that the derived latent features lead to improvement in both of the social media tasks.