Detecting discussion communities on vaccination in twitter

Vaccines have contributed to dramatically decrease mortality from infectious diseases in the 20th century. However, several social discussion groups related to vaccines have emerged, influencing the opinion of the population about vaccination for the past 20 years. These communities discussing on vaccines have taken advantage of social media to effectively disseminate their theories. Nowadays, recent outbreaks of preventable diseases such as measles, polio, or influenza, have shown the effect of a decrease in vaccination rates. Social Networks are one of the most important sources of Big Data. Specifically, Twitter generates over 400 million tweets every day. Data mining provides the necessary algorithms and techniques to analyse massive data and to discover new knowledge. This work proposes the use of these techniques to detect and track discussion communities on vaccination arising from Social Networks. Firstly, a preliminary analysis using data from Twitter and official vaccination coverage rates is performed, showing how vaccine opinions of Twitter users can influence over vaccination decision-making. Then, algorithms for community detection are applied to discover user groups opining about vaccines. The experimental results show that these techniques can be used to discover social discussion communities providing useful information to improve immunization strategies. Public Healthcare Organizations may try to use the detection and tracking of these social communities to avoid or mitigate new outbreaks of eradicated diseases.     

An e-patient’s End-user community (EUCY): The value added of social network applications

Decreasing revenues and increasing expenses has led many healthcare organizations to adopt newer technological applications in order to address the informational needs of their patients. One such adoption technique is to develop a more robust e-patient environment. Health care organizations may increase their effectiveness in meeting the needs of a growing e-patient population through the implementation of high-quality social networking applications such as Twitter. These applications may help to support and maintain a valuable and informed community. A literature review identifies three characteristics that have an impact on information exchange inherent to social networks: number of members, contact frequency, and type of knowledge. Data from a case study of a juvenile diabetic using Twitter helps to demonstrate these aforementioned characteristics. A framework is developed that may be used by health care organizations to better align social network objectives with expectations of an End user community (EUCY). Managerial implications of this study are discussed that can help information technology professionals as well as health administrators when implementing social networks.     

Information,Community, and Action: How Nonprofit Organizations Use Social Media*

The rapid diffusion of “microblogging” services such as Twitter is ushering in a new era of possibilities for organizations to communicate with and engage their core stakeholders and the general public. To enhance understanding of the communicative functions microblogging serves for organizations, this study examines the Twitter utilization practices of the 100 largest nonprofit organizations in the United States. The analysis reveals there are three key functions of microblogging updates—“information,”“community,” and “action.” Though the informational use of microblogging is extensive, nonprofit organizations are better at using Twitter to strategically engage their stakeholders via dialogic and community‐building practices than they have been with traditional websites. The adoption of social media appears to have engendered new paradigms of public engagement.     

Tweeting during food crises: A psychosocial analysis of threat coping expressions in Spain,during the 2011 European EHEC outbreak

Food crises imply responses that are not what people and organisations would normally do, if one or more threats (health, economic, etc.) were not present. At an individual level, this motivates individuals to implement coping strategies aimed at adaptation to the threat that has been presented, as well as the reduction of stressful experiences. In this regard, microblogging channels such as Twitter emerge as a valuable resource to access individuals' expressions of coping. Accordingly, Twitter expressions are generally more natural, spontaneous and heterogeneous — in cognitive, affective and behavioural dimensions — than expressions found on other types of social media (e.g. blogs). Moreover, as a social media channel, it provides access not only to an individual but also to a social level of analysis, i.e. a psychosocial media analysis. To show the potential in this regard, our study analysed Twitter messages produced by individuals during the 2011 EHEC/Escherichia coli bacteria outbreak in Europe, due to contaminated food products. This involved more than 3100 cases of bloody diarrhoea and 850 of haemolytic uraemic syndrome (HUS), and 53 confirmed deaths across the EU. Based on data collected in Spain, the country initially thought to be the source of the outbreak, an initial quantitative analysis considered 11,411 tweets, of which 2099 were further analysed through a qualitative content analysis. This aimed at identifying (1) the ways of coping expressed during the crisis; and (2) how uncertainty about the contaminated product, expressed through hazard notifications, influenced the former. Results revealed coping expressions as being dynamic, flexible and social, with a predominance of accommodation, information seeking and opposition (e.g. anger) strategies. The latter were more likely during a period of uncertainty, with the opposite being true for strategies relying on the identification of the contaminated product (e.g. avoid consumption/purchase). Implications for food crisis communication and monitoring systems are discussed.     

We Face,I Tweet: How Different Social Media Influence Political Participation through Collective and Internal Efficacy

This study advances a theoretical model centered on collective and internal efficacy to explain the separate pathways through which political sharing on Facebook and Twitter may influence individuals to engage in political activities. We test the model with data from a 2‐wave panel survey conducted with an adult population in 2013 in Chile. We found that frequent usage of Facebook and Twitter for sharing political information is conducive to higher levels of participation through different efficacy measures. Facebook has a significant effect on collective—not internal—efficacy, whereas Twitter's effect is on internal—not collective—efficacy. Results are discussed in light of the diverse affordances and strengths of network ties of Facebook and Twitter.     

Querying dynamic communities in online social networks简

Online social networks （OSNs） offer people the opportunity to join communities where they share a common interest or objective. This kind of community is useful for studying the human behavior, diffusion of information, and dynamics of groups. As the members of a community are always changing, an efficient solution is needed to query information in real time. This paper introduces the Follow Model to present the basic relationship between users in OSNs, and combines it with the MapReduce solution to develop new algorithms with parallel paradigms for querying. Two models for reverse relation and high-order relation of the users were implemented in the Hadoop system. Based on 75 GB message data and 26 GB relation network data from Twitter, a case study was realized using two dynamic discussion communities：#musicmonday and #beatcancer. The querying performance demonstrates that the new solution with the implementation in Hadoop significantly improves the ability to find useful information from OSNs.     

Information control and terrorism: Tracking the Mumbai terrorist attack through twitter

This paper analyzes the role of situational information as an antecedent of terrorists’ opportunistic decision making in the volatile and extreme environment of the Mumbai terrorist attack. We especially focus on how Mumbai terrorists monitored and utilized situational information to mount attacks against civilians. Situational information which was broadcast through live media and Twitter contributed to the terrorists’ decision making process and, as a result, increased the effectiveness of hand-held weapons to accomplish their terrorist goal. By utilizing a framework drawn from Situation Awareness (SA) theory, this paper aims to (1) analyze the content of Twitter postings of the Mumbai terror incident, (2) expose the vulnerabilities of Twitter as a participatory emergency reporting system in the terrorism context, and (3), based on the content analysis of Twitter postings, we suggest a conceptual framework for analyzing information control in the context of terrorism.     

Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters

The objective of this study is to examine and quantify the relationships among sociodemographic factors, damage claims, and social media attention on areas during natural disasters. Social media has become an important communication channel for people to share and seek situational information to learn of risks, to cope with community disruptions, and to support disaster response. Recent studies in disaster informatics have recognized the presence of bias in the representation of social media activity in areas affected by disasters. To explore related factors for such bias, existing studies have used geo-tagged tweets to assess the extent of social media activity in disaster-affected areas to evaluate whether vulnerable populations remain silent on social media. However, less than 1% of all tweets are actually geo-tagged; therefore, attempts to understand the representativeness of geotagged tweets to the general population have shown that certain populations are over- or underrepresented. To address this limitation, this study examined the attention given to locations based on social media content. The study conducted a content-based analysis to filter tweets related to 84 super-neighborhoods in Houston during Hurricane Harvey and 57 cities in North Carolina during Hurricane Florence. By examining the relationships among sociodemographic factors, the number of damage claims, and the volume of tweets, the results showed that social media attention concentrates in populous areas, independent of education, language, unemployment, and median income. The relationship between population and social media attention is characterized by a sub-linear power law, indicating a large variation among the sparsely populated areas. Using a machine-learning model to label the topics of the tweets, the results showed that social media users pay more attention to rescue- and donation-related information; nevertheless, the topic variation is consistent across areas with different levels of attention. These findings contribute to a better understanding of the spatial concentration of social media attention regarding posting and spreading situational information in disasters. The findings could inform emergency managers and public officials to effectively use social media data for equitable resource allocation and action prioritization.     

Learning sequential features for cascade outbreak prediction

Information cascades are ubiquitous in various online social networks. Outbreak of cascades could cause huge and unexpected effects. Therefore, predicting the outbreak of cascades at early stage is of vital importance to avoid potential bad effects and take relevant actions. Existing methods either adopt regression or classification technique with exhaustive feature engineering or predict cascade dynamics via modeling the stochastic process of cascades using a hard-coded diffusion–reaction function. One salient issue of these methods is that these methods heavily depend on human-defined knowledge, features or functions. In this paper, we propose to use recurrent neural network with long short-term memory to directly learn sequential patterns from information cascades, working in a fully data-driven manner. With the learned sequential patterns, the outbreak of cascade could be accurately predicted. Extensive experiments on both Twitter and Sina Weibo datasets demonstrate that our method significantly outperforms state-of-the-art methods at the prediction of cascade outbreaks.     

PRIVACY LEAKAGE IN HEALTH SOCIAL NETWORKS

Members of health social networks may be susceptible to privacy leaks by the amount of information they leave behind. The threat to privacy increases when members of these networks reuse their pseudonyms in other social networks. The risk of re‐identifying users from such networks requires quantitative estimates to evaluate its magnitude. The estimates will enable managers and members of health social communities to take corrective measures. We introduce a new re‐identification attack, the social network attack, that takes advantage of the fact that users reuse their pseudonyms. To demonstrate the attack, we establish links between MedHelp and Twitter (two popular social networks) based on matching pseudonyms. We used Bayesian networks to model the re‐identification risk and used stylometric techniques to identify the strength of the links. On the basis of our model 7‐11. 8% of the MedHelp members in the sample population who reused their pseudonyms in Twitter were re‐identifiable compared with 1% who did not. The risk estimates were measured at the 5% risk threshold. Our model was able to re‐identify users with a sensitivity of 41% and specificity of 96%. The potential for re‐identification increases as more data is accumulated from these profiles, which makes the threat of re‐identification more serious.     

How to find people who can help to answer a question? – Analyses of metrics and machine learning in online communities

Online communities have become important places for users to exchange information and build knowledge. In these communities, people ask and answer questions, learn with each other, but some problems may occur such as not getting an answer or getting contradictory ones. In order to increase the responsiveness of the communities, it would be important to identify people who are willing to help and who provide good answers in such communities, whom we call reliable users. We investigated various components of online communities and users’ attributes looking for a correlation between these characteristics and the users’ reputation in these communities. After that, we proposed the usage of two machine learning techniques, artificial neural network and clustering algorithm, with the users’ attributes for finding reliable sources. The results show that the usage of an artificial neural network is a good approach as around 90% of the users were correctly identified while the clustering algorithm makes to find groups of reliable users more easily.     

Twitter data laid almost bare: An insightful exploratory analyser

In today’s world, social networks and online communities continuously generate tons of data that reflect users’ habits, personal interests, opinions and emotions. However, little profit can be gained from such huge raw data collections unless we are able to translate them into useful knowledge. Microblogs like Twitter have recently attracted a great body of research works to mine useful insights about users interests and preferences in different geographical areas and time periods. Indeed, the rather heterogeneous dimensions characterizing Twitter data, such as space, time and text content, impose innovative methods in the data mining discovery process.This paper presents TCharM, a data analytics methodology based on cluster analysis and association rule discovery to gain interesting knowledge from large collections of Twitter data. TCharM explores tweet collections along the three dimensions characterizing tweets (i.e., text content, posting time and place) to support context-aware topic trend analysis. To discover groups of tweets with a good cohesion on the three tweet features, TCharM exploits a novel distance measure (TASTE) which allows driving the clustering task by considering in one step the three tweet features. Association rule analysis is then exploited to concisely describe the cluster content with a set of understandable and significant patterns which reveal underlying correlations among frequent topics, tweeting times and places. TCharM can provide useful information to understand the evolution of people’s involvement in different topics, across geographical areas and over time. TCharM find applications in various domains by providing a valuable support in decision making to domain experts. The experimental evaluation performed on real datasets demonstrates the effectiveness of the proposed approach in discovering cohesive clusters and actionable knowledge from Twitter data.     

A framework for designing closed domain virtual assistants

Since its beginning in 1969, the Internet has grown rapidly, especially over the past few years. Companies and organizations store more and more information about themselves on the Internet. Sometimes, that information is not well organized. Other times, the huge volume of available data makes useful information difficult to be found. The result is that users have to waste their time looking for what they want to know using the traditional menu-driven navigation and keyword search that websites provide. This is a critical issue because it decreases users interest about companies. In order to avoid this problem, in this paper we propose a framework for designing virtual assistants, which are, considering first results, an ideal alternative to help users find, not only the information that they are looking for, but also some related information which could be of the highest interest.     

A systems ergonomics analysis of the Maidstone and Tunbridge Wells infection outbreaks

This paper describes a systems ergonomics analysis of the recent outbreaks of Clostridium difficile, which occurred over the period 2005–07 within the UK Maidstone and Tunbridge Wells NHS Trust. The analysis used documents related to the outbreak, alongside the construction of a system model in order to probe deeper into the nature of contributory factors within the Trust. The findings from the analysis demonstrate the value of looking further at cross-level and whole-system aspects of infection outbreaks. In particular, there is a need for further study of the causal relationships that exist between hospital management and clinical management levels within the system. Finally, the paper discusses ways forward and strategies that could be adopted in order to limit the outbreak of hospital-related infections and shape future research. The approach used for the system analysis described in the paper could be used by healthcare practitioners and ergonomists to probe deeper into the causes of infection outbreaks and to extend the scope of interventions aimed at preventing their occurrence.     

Taking advantage of collective knowledge in emergency response systems

When large groups work on a theme, they have the potential to produce a lot of useful knowledge, regardless of whether they are acting in a coordinated manner or individually. Spontaneously generated information has received much attention in recent years, as organizations and businesses discover the power of crowds. New technologies, such as blogs, Twitter, wikis, photo sharing, collaborative tagging and social networking sites, enable the creation and dissemination of content in a relatively simple way. As a result, the aggregate body of knowledge is growing at an accelerated rate. Many organizations are looking for ways to harness this power, which is being called collective intelligence. Research has shown that it is possible to obtain high quality results from collectively produced work.In this paper, we consider the domain of emergency response. Research has shown that individuals respond quickly and massively to emergencies, and that they try to help with the situation. Thus, it seems like a logical step to attempt to harness collective knowledge for emergency management. Disaster relief groups and field command frequently suffer from lack of up to date information, which may be critical in a rapidly evolving situation. Some of this information could be generated by the crowd at large, enabling more effective response to the situation. In this paper, we discuss the possibilities for the introduction of collective knowledge in disaster relief and present architecture and examples of how this could be accomplished.     

Selected Bioterrorism Web Sites for the Health Care Community and Consumer

SUMMARY

For the people of the United States, the threat of bio-terrorism has become a reality. To respond to the recent outbreak of anthrax cases and to prepare for future threats, the health care community, civil authorities, and general public need access to reliable, up-to-date information. The Web is one tool that can be used to deliver this information. This article briefly defines bioterrorism, identifies major biological agents, looks at the potential impact of an attack and provides a selected list of Web sites for consumers and health care professionals. The selection criteria used to evaluate the sites included sponsorship, currency, content (factual information), and audience. Most of the sites are from government organizations, educational institutions, or professional associations.     

Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey,Irma, and Maria

ABSTRACT

People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyse high-volume and high-velocity data streams, dealing with information overload, among others. To eliminate such limitations, in this work, we first show that textual and imagery content on social media provide complementary information useful to improve situational awareness. We then explore ways in which various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields can exploit such complementary information generated during disaster events. Finally, we propose a methodological approach that combines several computational techniques effectively in a unified framework to help humanitarian organisations in their relief efforts. We conduct extensive experiments using textual and imagery content from millions of tweets posted during the three major disaster events in the 2017 Atlantic Hurricane season. Our study reveals that the distributions of various types of useful information can inform crisis managers and responders and facilitate the development of future automated systems for disaster management.     

What happens where during disasters? A Workflow for the multifaceted characterization of crisis events based on Twitter data

Twitter data are a valuable source of information for rescue and helping activities in case of natural disasters and technical accidents. Several methods for disaster‐ and event‐related tweet filtering and classification are available to analyse social media streams. Rather than processing single tweets, taking into account space and time is likely to reveal even more insights regarding local event dynamics and impacts on population and environment. This study focuses on the design and evaluation of a generic workflow for Twitter data analysis that leverages that additional information to characterize crisis events more comprehensively. The workflow covers data acquisition, analysis and visualization, and aims at the provision of a multifaceted and detailed picture of events that happen in affected areas. This is approached by utilizing agile and flexible analysis methods providing different and complementary views on the data. Utilizing state‐of‐the‐art deep learning and clustering methods, we are interested in the question, whether our workflow is suitable to reconstruct and picture the course of events during major natural disasters from Twitter data. Experimental results obtained with a data set acquired during hurricane Florence in September 2018 demonstrate the effectiveness of the applied methods but also indicate further interesting research questions and directions.     

Twitter-aided decision making: a review of recent developments

Twitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms.

     

Domain-specific sentiment analysis for tweets during hurricanes (DSSA-H): A domain-adversarial neural-network-based approach

Hurricanes are one of the most frequent and destructive disasters in the United States. The events are large scale and have relatively long-term impacts. Social networking platforms such as Twitter can provide real-time information for disaster managers and affected populations during large-scale disasters (e.g., hurricanes), but extracting useful information and interpreting data accurately for disaster management is still challenging. Sentiment analysis of social media data helps detect the concerns of affected people and understand individuals' responses on the ground at unprecedented scales, but the method is known to be domain-dependent. The same words or expressions can indicate opposite sentiments in different domains. This paper proposes a domain-specific sentiment analysis approach specifically for tweets posted during hurricanes (DSSA-H). DSSA-H can retrieve hurricane-relevant tweets with a trained supervised-learning classifier, Random Forest (RF), and classify the sentiment of hurricane-relevant tweets based on a domain-adversarial neural network (DANN). We built a dataset of tweets posted during six recent hurricanes and applied the DSSA-H approach for sentiment analysis. After evaluation, we found that each classifier (i.e., RF and DANN) outperforms baseline classifiers and that DSSA-H outperforms two high-performing general sentiment classification approaches when classifying sentiments of tweets posted during hurricanes. We also applied DSSA-H in examining sentiment patterns across six recent hurricanes in the U.S. This domain-specific sentiment analysis approach can be used by the first responders and affected communities to more accurately and rapidly detect crises and emergent events, allocate resources, and assess disaster's impact during hurricanes. DSSA-H contributes to an intelligent and adaptive disaster information system for the data-rich human and the built environment system.