Web-based Visual Analytics for Social Media Data

Social media data provides valuable information about different events, trends and happenings around the world. Visual data analysis tasks for social media data have large computational and storage space requirements. Due to these restrictions, subdivision of data analysis tools into several layers such as Data, Business Logic or Algorithms, and Presentation Layer is often necessary to make them accessible for variety of clients. On server side, social media data analysis algorithms can be implemented and published in the form of web services. Visual Interface can then be implemented in the form of thin clients that call these web services for data querying, exploration, and analysis tasks. In our work, we have implemented a web-based visual analytics tool for social media data analysis. Initially, we extended our existing desktop-based Twitter data analysis application named “ScatterBlog” to create web services based API that provides access to all the data analysis algorithms. In the second phase, we are creating web based visual interface consuming these web services. Some major components of the visual interface include map view, content lens view, abnormal event detection view, Tweets summary view and filtering / visual query module. The tool can then be used by parties from various fields of interest, requiring only a browser to perform social media data analysis tasks

Strategies and challenges for constructing and collecting visual corpora from image-based social media platforms

Visual elements play an important role within the multimodal nature of social media (Pearce et al., 2020). A growing body of research has focused on the analysis of still and moving images from different social media platforms from various perspectives of communication and media studies (Hautea, Parks, Takahashi, & Zeng, 2021; Li & Xie, 2020; Veum & Undrum, 2018). Although the aforementioned studies describe visual data collection, their principal focus does not rely on this collection, but on data analysis. Little attention has been paid to the challenges of collecting visual datasets (Highfield & Leaver, 2016). In this paper, I propose a methodological overview of several strategies for collecting large corpora of visual data from image-based social media platforms. Provided with exemplary publications, I review five strategies for collecting visual corpora: hashtag-based, account-based, metadata-based, random sampling, and mixed approach. Lastly, I present a case study with my own mixed approach to the collection of visual data from Instagram. Considering the usage, advantages and limitations of each strategy, the article will contribute to the developing science of social media research. I believe that a literature analysis of visual data collection strategies and a provided case study can help researchers optimize visual data collection from image-based social media

Studi Komparasi Media Pembelajaran Visual Dan Audio Terhadap Hasil Belajar IPS Siswa Kelas III SD Muhammadiyah 1 Ketelan Tahun 2015/2016

ABSTRACT Norma Ayunita / A510120021. COMPARISON STUDY OF LEARNING MEDIA VISUAL AND AUDIO TO IPS LEARNING OUTCOMES OF CLASS III SD MUHAMMADIYAH 1 KETEALAN YEAR 2015/2016. Essay. The Faculty of Education, University of Muhammadiyah Surakarta. January, 2016. The purpose of this study to determine: (1) the difference in the results of social studies using visual and audio media on a third-grade students of SD Muhammadiyah 1 Ketelan year 2015/2016. (2) better learning media used to learning outcomes IPS third grade students of SD Muhammadiyah 1 Ketelan year 2015/2016. This research is a quantitative research design eksperrimen. The research subject is class III C and D class III SD Muhammadiyah 1 Ketelan. Data collection techniques were used that test and documentation. Test instrument used in this study is to test the validity and reliability. Data analysis techniques using T test. as the terms of data analysis performed using test Liliefors normality test and homogeneity test using Bartlett test. Based on the analysis of data in the 0.05 α showed no difference in the results of social studies using visual and audio media pembeljaran with t_obs= 0,165 < t_tabel= 2,00172. The average results of social studies using visual media is 74 and the average value of the results of social studies using the audio media is 71. So better visual learning media use the results of social studies students of class III rather than using the audio media. Keywords : visual media , audio media , learning outcomes IP

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

'Scrapbooks' as a resource in media research with young people

[About the book]: Visual media offer powerful communication opportunities. Doing Visual Research with Children and Young People explores the methodological, ethical, representational and theoretical issues surrounding image-based research with children and young people. It provides well-argued and illustrated resources to guide novice and experienced researchers through the challenges and benefits of visual research. Because new digital technologies have made it easier and cheaper to work with visual media, Pat Thomson brings together an international body of leading researchers who use a range of media to produce research data and communicate findings. Situating their discussions of visual research approaches within the context of actual research projects in communities and schools, and discussing a range of media from drawings, painting, collage and montages to film, video, photographs and new media, the book offers practical pointers for conducting research. These include: - why visual research is used - how to involve children and young people as co–researchers - complexities in analysis of images and the ethics of working visually - institutional difficulties that can arise when working with a 'visual voice' - how to manage resources in research projects Doing Visual Research with Children and Young People will be an ideal guide for researchers both at undergraduate and postgraduate level across disciplines, including education, youth and social work, health and nursing, criminology and community studies. It will also act as an up-to-date resource on this rapidly changing approach for practitioners working in the field

Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.Comment: 9 pages, 5 figures, AAAI 201

Researching Visual Social Media Platforms

Dhiraj Murthy is an Associate Professor of Journalism and Sociology at the University of Texas at Austin. He founded and directs the Computational Media Lab there. Murthy’s research explores social media, computational social science, race/ethnicity, qualitative/mixed methods, and disasters. Dr. Murthy has edited 3 journal special issues and authored over 60 articles, book chapters, and papers. Murthy wrote the first scholarly book about Twitter (second edition published by Polity Press, 2018). He is currently funded by the National Science Foundation’s Civil, Mechanical and Manufacturing Innovation (CMMI) Division for pioneering work on using the social media networks of journalists for damage reconnaissance during Hurricane Florence. Dr. Murthy’s work also uniquely explores the potential role of social technologies in diversity and community inclusion.With the meteoric rise of Instagram, Snapchat and YouTube, it is clear that image- and video- based platforms have become tremendously important to our social, political, and economic lives. However, there are unique challenges associated with data collection and analysis on visual social media platforms. This workshop explores the following questions in detail: How do we integrate and weigh Big Data questions with more in-depth contextualized analysis of social media content? How do we categorize textual and visual content, addressing issues of ontology? How can we scale small data to big data in visual spaces? Ultimately, it is argued that image/video data produced and consumed on social media has real value in helping us understand the social experience of everyday and profound events, but studying these types of data often requires innovations in theory and methods. Hands-on methods work will involve participants collecting data from YouTube and understanding structured metadata and unstructured data involving visual content

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