Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Batch to Real-Time: Incremental Data Collection & Analytics Platform

Real-time data collection and analytics is a desirable but challenging feature to provide in data-intensive software systems. To provide highly concurrent and efficient real-time analytics on streaming data at interactive speeds requires a well-designed software architecture that makes use of a carefully selected set of software frameworks. In this paper, we report on the design and implementation of the Incremental Data Collection & Analytics Platform (IDCAP). The IDCAP provides incremental data collection and indexing in real-time of social media data; support for real-time analytics at interactive speeds; highly concurrent batch data processing supported by a novel data model; and a front-end web client that allows an analyst to manage IDCAP resources, to monitor incoming data in real-time, and to provide an interface that allows incremental queries to be performed on top of large Twitter datasets

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis

Web-Based Interactive Social Media Visual Analytics

Real-time social media platforms enable quick information broadcasting and response during disasters and emergencies. Analyzing the massive amount of generated data to understand the human behavior requires data collection and acquisition, parsing, filtering, augmentation, processing, and representation. Visual analytics approaches allow decision makers to observe trends and abnormalities, correlate them with other variables and gain invaluable insight into these situations. In this paper, we propose a set of visual analytic tools for analyzing and understanding real-time social media data in times of crisis and emergency situations. First, we model the degree of risk of individuals’ movement based on evacuation zones and post-event damaged areas. Identified movement patterns are extracted using clustering algorithms and represented in a visual and interactive manner. We use Twitter data posted in New York City during Hurricane Sandy in 2012 to demonstrate the efficacy of our approach. Second, we extend the Social Media Analytics and Reporting Toolkit (SMART) to supporting the spatial clustering analysis and temporal visualization. Our work would help first responders enhance awareness and understand human behavior in times of emergency, improving future events’ times of response and the ability to predict the human reaction. Our findings prove that today’s high-resolution geo-located social media platforms can enable new types of human behavior analysis and comprehension, helping decision makers take advantage of social media

MINING FACEBOOK PAGE FOR BI-PARTISAN ANALYSIS

Social media, particularly Facebook, has become ubiquitous in everyday life. Almost all news sources have adopted Facebook as a platform for dissemination of news. There are many opinions and studies on the partisanship of journalism. What makes social media interesting is that people do not only consume but also interact with others centered around a news article or post. Depending on the partisan bias of both the provider and the consumer, the interactions, and thus the conversation may vary. This research is a preliminary step towards mining these interactions and conversations pivoted against the topic of “fake news” from CNN and Fox News. We used several techniques of data mining, data analytics, and text analytics to generate summaries and descriptive statistics to explore user behavior. Our findings suggest that CNN follower base is more interactive and gregarious. Additionally, CNN followers’ use of Facebook reactions is more diverse, favoring the “haha” (funny / sarcastic) reaction, while those on Fox News’ inclined more towards “like” and “love” (agreement)

Is content king? Job seekers’ engagement with social media employer branding content

Resumen de la ponencia[EN] Increasing digitization and the emergence of social media have radically changed the recruitment landscape adding interactive digital platforms to traditional means of employer communication. Removing barriers of distance and timing, social media enable firms to continue their efforts of promoting their employment brand online. However, social media employer communication and employer brand building remains woefully understudied. Our study addresses this gap by investigating how firms use social media to promote their employer brand. We analyze employer branding communication in a sample of N = 216,828 human resources (HR) related Tweets from N = 166 Fortune 500 companies. Using supervised machine learning we classify the Tweet content according to its informational and inspirational nature, identifying five categories of employer branding social media communication on Twitter.Moser, K.; Tumasjan, A.; Welpe, I. (2016). Is content king? Job seekers’ engagement with social media employer branding content. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 124-124. https://doi.org/10.4995/CARMA2016.2016.3103OCS12412

CSILM: Interactive Learning Modules For Computer Science

CSILM is an online interactive learning management system designed to help students learn fundamental concepts of computer science. Apart from learning computer science modules using multimedia, this online system also allows students talk to professors using communication mediums like chat and implemented web analytics, enabling teachers to track student behavior and see student’s interest in learning the modules,. Integrating social media in to the existing portal also makes it possible for students to share the modules they have created, helping students work together