Space, time, and disease on social media: a case study of dengue fever in China

GEOMATICA ◽  
2018 ◽  
Vol 72 (4) ◽  
pp. 112-126
Author(s):  
Junfang Gong ◽  
Shengwen Li ◽  
Jay Lee
Keyword(s):  
Social Media ◽  
Diffusion Process ◽  
Information Diffusion ◽  
Standard Form ◽  
Spatial Relationship ◽  
Spatiotemporal Patterns ◽  
Spatial And Temporal Patterns ◽  
Dynamic Events ◽  
Spatiotemporal Trends ◽  
Social Events

It is possible to generate real-time and location-by-location data of many types of human dynamic events based on social media information for the awareness of events in public health. Analyzing these events is useful in understanding spatiotemporal trends and patterns of how diseases spread and also provides indications for users’ sentiment about the concerned disease. This article examines the spatial and temporal patterns of social media posts based on the content, attributes, and follower activities of posts on social media. We describe the spatial features of the topic discussed in the posts and the spatial relationship among comments on the posts. We present models for describing the diffusion process of these posts and for exploring their spatiotemporal patterns. Our results suggest that (1) the long-term trends of the topics in users’ views seem to be stable, (2) results from analyzing follower activities of posts are critical in describing the spatial patterns of the posts, and (3) the diffusion process of an event in social media is still similar to that of a traditional information diffusion model. Our findings are useful for understanding social media and social events. The processes we describe in this article suggest a standard form of analysis that can be adopted for extracting spatiotemporal patterns of information diffusion and for data mining in social media posts.

scholarly journals SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

2018 ◽  
Vol 10 (8) ◽  
pp. 2731 ◽  
Author(s):  
Berny Carrera ◽  
Jae-Yoon Jung
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Diffusion Process ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Latent Dirichlet Allocation ◽  
Probabilistic Approach ◽  
Digital Era ◽  
Online Social Media ◽  
User Comments

In this digital era, people can become more interconnected as information spreads easily and quickly through online social media. The rapid growth of the social network services (SNS) increases the need for better methodologies for comprehending the semantics among the SNS users. This need motivated the proposal of a novel framework for understanding information diffusion process and the semantics of user comments, called SentiFlow. In this paper, we present a probabilistic approach to discover an information diffusion process based on an extended hidden Markov model (HMM) by analyzing the users and comments from posts on social media. A probabilistic dissemination of information among user communities is reflected after discovering topics and sentiments from the user comments. Specifically, the proposed method makes the groups of users based on their interaction on social networks using Louvain modularity from SNS logs. User comments are then analyzed to find different sentiments toward a subject such as news in social networks. Moreover, the proposed method is based on the latent Dirichlet allocation for topic discovery and the naïve Bayes classifier for sentiment analysis. Finally, an example using Facebook data demonstrates the practical value of SentiFlow in real world applications.

Predicting the Trends of Social Events on Chinese Social Media

2017 ◽  
Vol 20 (9) ◽  
pp. 533-539 ◽  
Author(s):  
Yang Zhou ◽  
Lei Zhang ◽  
Xiaoqian Liu ◽  
Zhen Zhang ◽  
Shuotian Bai ◽  
...  
Keyword(s):  
Social Media ◽  
Social Events ◽  
Chinese Social Media

scholarly journals How Social Media Changes Today’s Marketing Strategies

2020 ◽  
Vol 5 (9) ◽  
pp. 725-736
Author(s):  
Nasr Abdulaziz Murshed
Keyword(s):  
Social Media ◽  
Marketing Strategies ◽  
Target Market ◽  
Full Time ◽  
Positive Information ◽  
Social Events ◽  
Social Penetration ◽  
Shared Information ◽  
The Media ◽  
Awareness Raising

In the past recent years, WhatsApp and WeChat have surprisingly fast growth. Facebook as well became the first social network to reach 1 billion active users every month. The presence of social media is an expectation for brands instead of an exception to the rule. Social events and shared information within your target market will help you understand developments in the industry. The opportunity to expose patterns in business in real time is a potential business intelligence goldmine. The worldwide rate of social penetration reached 49% in 2020, with the highest penetration rates in East Asia and North America. Instagram enables users, through their standards of credibility, authenticity and transparency, to develop themselves. Influencers from social media have a personal recognizable identity, also known as the "true brand" An influencer has tools and values that can motivate many other followers to increase their presence in the media. Even if these leads do not directly buy via social, awareness-raising can lead them to become full-time buyers. The overwhelming majority of users in Instagram are under the age of 30 according to recent Social Media demographics. Marketers face a dilemma: more and more people want businesses to take a social stand, but 79% of CMOs fear that their capacity to attract consumers will be adversely affected. Businesses can mitigate negative emotions by providing positive information to popular social media users. Marketing managers will encourage consumers through tournament and influencer programmers to engage in contact practices so customers can evangelize and encourage their loyalty to the organization through the creation and delivery of user-generated content

scholarly journals Sensing And Detecting Small-Scale Events Using Geosocial Media Data

2021 ◽  
Author(s):  
Shishuo Xu
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Human Movement ◽  
Small Scale ◽  
Type I ◽  
Social Event ◽  
Ground Truth Data ◽  
Social Events ◽  
Social Media Platforms ◽  
Media Data

<div>Small-scale events involve interactive human movement in limited space and time. Social media platforms possibly generate large amount of geospatially-referenced information related to small-scale events. It benefits individuals, management departments, and urban systems if small-scale events can be timely detected from social media platforms, where measuring the abnormal patterns of human movement to discover events and analyzing associated texts to interpret the reasons behind abnormal movement are two keys. Through investigating how people move as different events occur and measuring the patterns on social media platforms, small-scale events can be generally classified into two types, namely type I events with abrupt patterns and type II events with random occurrence of key factors, where social events and traffic events are representative correspondingly.</div><div>Despite many studies have been conducted to detect social events and traffic events using geosocial media data, there still are some un-answered questions requiring further research. Most existing studies did not identify occurring events from a full coverage of spatial, temporal, and semantic perspectives. Studies concerning social event detection lack efficient semantic analysis summarizing event content to infer the reasons driving the abnormal movement. The typical classification-based method regarding traffic event detection lacks investigation on how the spatiotemporal distribution of traffic relevant posts associate with the occurring traffic events, and simply assigns the detected events with predefined categories, missing events that indicate traffic anomalies but go beyond the predetermined categories.<br></div><div>In this thesis, spatial-temporal-semantic approaches are proposed to measure spatiotemporal patterns of posts and users of social media platforms to capture abnormal human movement, and analyze the content of associated posts to mine the reasons driving the movement. A variety of techniques including machine learning, natural language processing, and spatiotemporal analysis are adopted to realize effective detection. Based on one-year Twitter data collected in Toronto, 2014 Toronto International Film Festival and traffic anomaly detection are selected as two case studies to evaluate the performance of proposed approaches. Through comparing with the ground truth data, the result reveals that more than 80% of the detected events do refer to real-world events, which illustrates the feasibility and efficiency of proposed approaches.<br></div><div><br></div><div>Keywords: Small-scale event, Event detection, Geosocial media data, Traffic event, Social event, Twitter, Spatiotemporal clustering<br></div>

scholarly journals Information Diffusion on Social Media During Natural Disasters

2018 ◽  
Vol 5 (1) ◽  
pp. 265-276 ◽  
Author(s):  
Rongsheng Dong ◽  
Libing Li ◽  
Qingpeng Zhang ◽  
Guoyong Cai
Keyword(s):  
Social Media ◽  
Natural Disasters ◽  
Information Diffusion

GWAP as a Tool to Analyze, Design, and Test Geo-Social Systems

2013 ◽  
pp. 380-407 ◽  
Author(s):  
Martina Deplano ◽  
Giancarlo Ruffo
Keyword(s):  
Social Media ◽  
Information Systems ◽  
Real Time ◽  
Mobile Devices ◽  
General Framework ◽  
Social Dynamics ◽  
Social Systems ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Social Events

In this chapter, the authors discuss the state-of-the-art of Geo-Social systems and Recommender systems, which are becoming extremely popular for users accessing social media trough mobile devices. Moreover, they introduce a general framework based on the interaction among those systems and the “Game With A Purpose” (GWAP) paradigm. The proposed framework/platform can help researchers to understand geo-social dynamics in order to design and test new services, such as recommenders of places of interest for tourists, real-time traffic information systems, personalized suggestions of social events, and so forth. To target the governance of such complexity, relevant data must be collected by the investigators, shared with the community, and analyzed to find dynamical patterns that correlate spatial-temporal information with the user’s preferences and objectives. The authors argue that the GWAP approach can be exploited to successfully satisfy many of these tasks.

Reconciling Social Media with Luxury Fashion Brands

2015 ◽  
pp. 917-945 ◽  
Author(s):  
Wilson Ozuem ◽  
Kerri Tan
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Cultural Transformation ◽  
Marketing Communications ◽  
Communication Media ◽  
Communications Media ◽  
Customer Expectations ◽  
Fashion Brand ◽  
Social Media Platforms ◽  
Human Association

Modern developments in communication media are creating new networks of information diffusion which are profoundly altering the way in which people can construct shared ‘realities'. Internet along with its prototypical subsets, notably social media, is enabling the emergence of new mechanism of human association which are shaped by – yet also shape – the development of this new medium of communication. This chapter integrates social media theory and luxury fashion brand theory arguments to examine the knowledge benefits that this cultural transformation provides to the development of a marketing communications programme. The authors argue that the key to providing an effective marketing communication programme is understanding and responding to customer expectations through the integration of social media platforms and traditional marketing communications media.

scholarly journals A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns

2021 ◽  
Vol 14 ◽  
Author(s):  
Xueyuan She ◽  
Saurabh Dash ◽  
Daehyun Kim ◽  
Saibal Mukhopadhyay
Keyword(s):  
Neural Network ◽  
Moving Objects ◽  
Short Term Memory ◽  
Back Propagation ◽  
Spike Timing ◽  
Spatiotemporal Patterns ◽  
Spiking Neural Network ◽  
Spatial And Temporal Patterns ◽  
Unsupervised Training ◽  
Improved Performance

This paper introduces a heterogeneous spiking neural network (H-SNN) as a novel, feedforward SNN structure capable of learning complex spatiotemporal patterns with spike-timing-dependent plasticity (STDP) based unsupervised training. Within H-SNN, hierarchical spatial and temporal patterns are constructed with convolution connections and memory pathways containing spiking neurons with different dynamics. We demonstrate analytically the formation of long and short term memory in H-SNN and distinct response functions of memory pathways. In simulation, the network is tested on visual input of moving objects to simultaneously predict for object class and motion dynamics. Results show that H-SNN achieves prediction accuracy on similar or higher level than supervised deep neural networks (DNN). Compared to SNN trained with back-propagation, H-SNN effectively utilizes STDP to learn spatiotemporal patterns that have better generalizability to unknown motion and/or object classes encountered during inference. In addition, the improved performance is achieved with 6x fewer parameters than complex DNNs, showing H-SNN as an efficient approach for applications with constrained computation resources.

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