SocioHub: An Interactive Tool for Cross-Platform Social Media Data Collection

Social media is inherently about connecting and interacting with others. Different social media platforms have unique characteristics and user bases. Moreover, people use different platforms for various social and entertainment purposes. Analyzing cross-platform user behavior can provide insights into the preferences and expectations of users on each platform. By understanding how users behave and interact across platforms, we can build an understanding of content consumption patterns, enhance communication and social interactions, and tailor platform-specific strategies. We can further gather insights into how users navigate and engage with their platforms on different devices. In this work, we develop a tool SocioHub, which enables users to gather data on multiple social media platforms in one place. This tool can help researchers gain insights into different data attributes for users across social media platforms such as Twitter, Instagram, and Mastodon. Keywords: Social Media Platforms, Twitter, Instagram, Mastodon.Comment: 5 pages, 2 figure

From social networks to emergency operation centers: A semantic visualization approach

Social networks are commonly used by citizens as a communication channel for sharing their messages about a crisis situation and by emergency operation centers as a source of information for improving their situation awareness. However, to utilize this source of information, emergency operators and decision makers have to deal with large and unstructured data, the content, reliability, quality, and relevance of which may vary greatly. In this paper, to address this challenge, we propose a visual analytics solution that filters and visualizes relevant information extracted from Twitter. The tool offers multiple visualizations to provide emergency operators with different points of view for exploring the data in order to gain a better understanding of the situation and take informed courses of action. We analyzed the scope of the problem through an exploratory study in which 20 practitioners answered questions about the integration of social networks in the emergency management process. This study inspired the design of a visualization tool, which was evaluated in a controlled experiment to assess its effectiveness for exploring spatial and temporal data. During the experiment, we asked 12 participants to perform 5 tasks related to data exploration and fill a questionnaire about their experience using the tool. One of the most interesting results obtained from the evaluation concerns the effectiveness of combining several visualization techniques to support different strategies for solving a problem and making decisions.This work was supported by the project PACE grant funded by the Spanish Ministry of Economy and Competitivity [TIN2016-77690-R]

Learning from information crises: Exploring aggregated trustworthiness in big data production

In a crisis situation when traditional venues for information dissemination aren't reliable and information is needed immediately "aggregated trustworthiness", data verification through network evaluation and social validation, becomes an important alternative. However, the risk with evaluating credibility through trust and network reputation is that the perspective can get biased. In these socially distributed information systems there is therefore of particularly high importance to understand how data is socially produced by whom. The purpose with the research project presented in this position paper is to explore how patters of bias in information production online can become more transparent by including tools that analyze and visualize aggregated trustworthiness. the research project consists of two interconnected parts. We will first look into a recent crisis situation, the case Red Hook after Hurricane Sandy, to see how the dissemination of information took place in the recovery work, focusing on questions of credibility and trust. Thereafter, this case study will inform the design of two collaborative tools where we investigate how social validation processes can be made more transparent

Twista – An Application for the Analysis and Visualization of Tailored Tweet Collections

Social media services like Twitter churn out user-generated content in vast amounts. The massive availability of this kind of data demands new forms of analysis and visualization, to make it accessible and interpretable. In this article, we introduce Twista, an application that can be used to create tailored tweet collections according to specific filter criteria, such as the occurrence of certain keywords or hashtags. Once the tweet collection has been created, Twista calculates basic statistics, e.g. the average tweet length or the most active user. Furthermore, the application can perform basic sentiment analysis, analyze tweets with regard to their date of publication, and analyze the communication between different Twitter users. The results of these analyses are visualized by means of the data driven documents toolkit (d3.js) and can be viewed directly in the browser, or are available for download in PDF and JSON format. We also present three exemplary use cases that illustrate the possible use of Twista for different scenarios

Giving meaning to tweets in emergency situations: a semantic approach for filtering and visualizing social data

In this paper, we propose a semantic approach for monitoring information publishedon social networks about a specific event. In the era of Big Data, when an emergencyoccurs information posted on social networks becomes more and more helpful foremergency operators. As direct witnesses of the situation, people share photos, videosor text messages about events that call their attention. In the emergency operationcenter, these data can be collected and integrated within the management processto improve the overall understanding of the situation and in particular of the citizenreactions. To support the tracking and analyzing of social network activities, there arealready monitoring tools that combine visualization techniques with geographicalmaps. However, tweets are written from the perspective of citizens and the informationthey provide might be inaccurate, irrelevant or false. Our approach tries to dealwith data relevance proposing an innovative ontology-based method for filteringtweets and extracting meaningful topics depending on their semantic content. In thisway data become relevant for the operators to make decisions. Two real cases used totest its applicability showed that different visualization techniques might be neededto support situation awareness. This ontology-based approach can be generalizedfor analyzing the information flow about other domains of application changing theunderlying knowledge base.This work is supported by the project emerCien grant funded by the Spanish Ministry of Economy and Competitivity (TIN2012-09687)

Information propagation in social networks during crises: A structural framework

In crisis situations like riots, earthquakes, storms, etc. information plays a central role in the process of organizing interventions and decision making. Due to their increasing use during crises, social media (SM) represents a valuable source of information that could help obtain a full picture of people needs and concerns. In this chapter, we highlight the importance of SM networks in crisis management (CM) to show how information is propagated through. The chapter also summarizes the current state of research related to information propagation in SMnetworks during crises. In particular three classes of information propagation research categories are identified: network analysis and community detection, role and topic-oriented information propagation, and infrastructure-oriented information propagation. The chapter describes an analysis framework that deals with structural information propagation for crisismanagement purposes. Structural propagation is about broadcasting specific information obtained from social media networks to targeted sinks/receivers/hubs like emergency agencies, police department, fire department, etc. Specifically, the framework aims to identify the discussion topics, known as sub-events, related to a crisis (event) from SM contents. A brief description of techniques used to detect topics and the way those topics can be used in structural information propagation are presented

Visual Analytics Methods for Exploring Geographically Networked Phenomena

abstract: The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models. Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201