Interactive Visualization and Analysis Dashboard for Social Media Networks and Multivariate Data
| dc.contributor.advisor | Cantu, Alma | |
| dc.contributor.author | Aldawsari, Luluh | |
| dc.date.accessioned | 2023-12-03T12:13:58Z | |
| dc.date.available | 2023-12-03T12:13:58Z | |
| dc.date.issued | 2023-12-01 | |
| dc.description | The purpose of this project is to provide a tool to explore multivariate aspects of networks. The tool will be modified to allow the visualization of network data. It will also be used to visualize the relationships between entities, the overall structure of graphs, and other metrics. The tool can be a valuable resource for experts or businesses that are seeking exploration, knowledge extraction, and community behaviors. It may help them to address the limitations in their ability to capture the complexity of community behaviors and facilitate their realization of the organization and interrelationships among communities. The resulting dashboard will offer advanced visualization techniques that may contribute towards depicting user behaviors and facilitate the interpretation of community behaviors. Moreover, it may be advantageous to use the previously mentioned measures and metrics with data that exhibits heterogeneity. The presented dashboard provides an analysis of network characteristics and node features. It can offer valuable insights for individuals, experts, and businesses seeking to comprehend the complex nature of community behaviors and effectively understand the organizational and connected dynamics across communities. The aim of this project is to visualize multivariate data (such as network data) in order to understand community behaviors. This will be accomplished by modifying an existing visualization tool to allow the visualization of network data using the d3 library that is in JavaScript. | |
| dc.description.abstract | Researching the fusion of data visualization, network analysis and interactive elements, especially when dealing with a significant number of nodes, is beneficial for examining the social behaviors and connections of people. Indeed, it can open avenues for future research into data-driven insights and targeted communication strategies. We introduce an interactive dashboard developed using the JavaScript d3.js library to visualize Facebook network data. The dashboard shows node metrics and network metrics to identify influential nodes through degree centrality and other metrics. Additionally, community detection using Louvain algorithms clusters the network into communities. Node features, representing various user characteristics, are visualized through a parallel coordinate plot, revealing trends and correlations. The dashboard is made interactive with tooltips, click events, and node dragging for an effortless user experience. Its success in presenting network structures and user insights holds promising implications for marketing strategies and decision-making. Overall, this research contributes to the field of data visualization and network analysis, offering valuable tools for understanding social network dynamics and optimizing digital communication strategies. The interactive dashboard empowers users to explore and gain valuable insights from complex network data, paving the way for future advancements in the realm of data-driven insights and targeted marketing efforts | |
| dc.format.extent | 32 | |
| dc.identifier.citation | . Abdullah, N.A.S. and Anuar, H.B., 2018. Review of Data Visualization for Social Media Postings. International Journal of Engineering & Technology, 7(4.38), pp.939-943. 2. Alaimo, C. and Kallinikos, J., 2017. Computing the everyday: Social media as data platforms. The Information Society, 33(4), pp.175-191. 3. Aris, K.A.W.M., Ramasamy, C., Aris, T.N.M. and Zolkepli, M., 2022. Dynamic Force-directed Graph with Weighted Nodes for Scholar Network Visualization. International Journal of Advanced Computer Science and Applications, 13(12). 4. Bahri, M., Bifet, A., Gama, J., Gomes, H.M. and Maniu, S., 2021. Data stream analysis: Foundations, major tasks and tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(3), p.e1405. 5. Berahmand, K., Bouyer, A. and Samadi, N., 2019. A new local and multidimensional ranking measure to detect spreaders in social networks. Computing, 101, pp.1711-1733. 6. Bing, A. and Li-Gu, Z., 2020, August. Film Big Data Visualization Based on D3. js. In 2020 International Conference on Big Data and Social Sciences (ICBDSS) (pp. 50-53). IEEE. 7. Bispo, J.A.B., Balise, R.R. and Kobetz, E.K., 2022. Cancer Data Visualization: Developing Tools to Serve the Needs of Diverse Stakeholders. Current Epidemiology Reports, pp.1-7. 8. Bostock, M., Ogievetsky, V. and Heer, J., 2011. D³ data-driven documents. IEEE transactions on visualization and computer graphics, 17(12), pp.2301-2309. 9. Broasca, L., Ancusa, V.M[. and Ciocarlie, H., 2019, October. A qualitative analysis on force directed network visualization tools in the context of large complex networks. In 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC) (pp. 656-661). IEEE. 10. Cantu, A., Micó-Amigo, E., Del, D. and Fernstad, S.J., 2023. Assemblies Plot, a visualization tool to explore categorical and quantitative data: application to digital mobility outcomes. In IEEE Pacific Visualization Symposium (PacificVis). Newcastle University. 11. Cao, N., Lu, L., Lin, Y.R., Wang, F. and Wen, Z., 2015. Socialhelix: visual analysis of sentiment divergence in social media. Journal of visualization, 18, pp.221-235. 12. Cao, N., Shi, C., Lin, S., Lu, J., Lin, Y.R. and Lin, C.Y., 2015. Targetvue: Visual analysis of anomalous user behaviors in online communication systems. IEEE transactions on visualization and computer graphics, 22(1), pp.280-289. 13. Chen, I.X. and Yang, C.Z., 2010. Visualization of social networks. Handbook of social network technologies and applications, pp.585-610. 14. Chen, S., Chen, S., Lin, L., Yuan, X., Liang, J. and Zhang, X., 2017, October. E-map: A visual analytics approach for exploring significant event evolutions in social media. In 2017 IEEE conference on visual analytics science and technology (VAST) (pp. 36-47). IEEE. 15. Chen, S., Chen, S., Wang, Z., Liang, J., Yuan, X., Cao, N. and Wu, Y., 2016, October. D-Map: Visual analysis of ego-centric informa15tion diffusion patterns in social media. In 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) (pp. 41-50). IEEE. 16. Cheong, S.H. and Si, Y.W., 2018, November. Snapshot Visualization of Complex Graphs with Force Directed Algorithms. In 2018 IEEE International Conference on Big Knowledge (ICBK) (pp. 139-145). IEEE. 17. Cruz, J.D., Bothorel, C. and Poulet, F., 2014. Community detection and visualization in social networks: Integrating structural and semantic information. ACM Transactions on Intelligent Systems and Technology (TIST), 5(1), pp.1-26. 18. Disney, A. (2020) Social network analysis: Understanding centrality measures, Cambridge Intelligence. Available at: https://cambridge-intelligence.com/keylines-faqs-social-network-analysis/ (Accessed: 7 August 2023). 19. Fikkert, W., D’Ambros, M., Bierz, T. and Jankun-Kelly, T.J., 2007. Interacting with visualizations. In Human-Centered Visualization Environments: GI-Dagstuhl Research Seminar, Dagstuhl Castle, Germany, March 5-8, 2006, Revised Lectures (pp. 77-162). Springer Berlin Heidelberg. 20. Flemisch, T., Langner, R., Alrabbaa, C. and Dachselt, R., 2020, November. Towards Designing a Tool For Understanding Proofs in Ontologies through Combined Node-Link Diagrams. In VOILA@ ISWC (pp. 28- 40). 21. Francalanci, C. and Hussain, A., 2017. Influence-based Twitter browsing with NavigTweet. Information Systems, 64, pp.119-131. 22. Fu, L., Jacobs, M.A., Brookover, J., Valente, T.W., Cobb, N.K. and Graham, A.L., 2017. An exploration of the Facebook social networks of smokers and non-smokers. PLoS One, 12(11), p.e0187332. 30 23. Ghani, S., Kwon, B.C., Lee, S., Yi, J.S. and Elmqvist, N., 2013. Visual analytics for multimodal social network analysis: A design study with social scientists. IEEE transactions on visualization and computer graphics, 19(12), pp.2032-2041. 24. Giridhar, P. and Abdelzaher, T., 2017, March. Visualization of events using Twitter and Instagram. In 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 82-84). IEEE. 25. Giuntini, F.T., de Moraes, K.L., Cazzolato, M.T., de Fátima Kirchner, L., Maria de Jesus, D., Traina, A.J., Campbell, A.T. and Ueyama, J., 2021. Modeling and assessing the temporal behavior of emotional and depressive user interactions on social networks. IEEE Access, 9, pp.93182-93194. 26. Gouvêa, A.M., da Silva, T.S., Macau, E.E. and Quiles, M.G., 2021. Force-directed algorithms as a tool to support community detection. The European Physical Journal Special Topics, 230(14-15), pp.2745-2763. 27. Guo, C., Yang, L., Chen, X., Chen, D., Gao, H. and Ma, J., 2020. Influential nodes identification in complex networks via information entropy. Entropy, 22(2), p.242. 28. Han, D., Pan, J., Zhao, X. and Chen, W., 2021. Netv. js: A web-based library for high-efficiency visualization of large-scale graphs and networks. Visual Informatics, 5(1), pp.61-66. 29. Henry, N. and Fekete, J.D., 2007. Matlink: Enhanced matrix visualization for analyzing social networks. In Human-Computer Interaction–INTERACT 2007: 11th IFIP TC 13 International Conference, Rio de Janeiro, Brazil, September 10-14, 2007, Proceedings, Part II 11 (pp. 288-302). Springer Berlin Heidelberg. 30. Huang, Z., Kucher, K. and Kerren, A., 2022. Towards an Exploratory Visual Analytics System for Multivariate Subnetworks in Social Media Analysis. In IEEE Visualization and Visual Analytics (VIS'22), Oklahoma City, USA (Hybrid), 16-21 October, 2022. IEEE. 31. Jiranantanagorn, P. and Puangsuwan, T., 2022, November. Thai Sentiment Analysis and Visualization of Movie Reviews From Social Media. In 2022 6th International Conference on Information Technology (InCIT) (pp. 100-104). IEEE. 32. Kang, J., Tang, L. and Fiore, A.M., 2014. Enhancing consumer–brand relationships on restaurant Facebook fan pages: Maximizing consumer benefits and increasing active participation. International Journal of Hospitality Management, 36, pp.145-155. 33. Kämpf, M., Tessenow, E., Kenett, D.Y. and Kantelhardt, J.W., 2015. The detection of emerging trends using Wikipedia traffic data and context networks. PloS one, 10(12), p.e0141892. 34. Kerren, A., Purchase, H.C. and Ward, M.O., 2014, April. Introduction to multivariate network visualization. In Multivariate Network Visualization: Dagstuhl Seminar# 13201, Dagstuhl Castle, Germany, May 12-17, 2013, Revised Discussions (pp. 1-9). Cham: Springer International Publishing. 35. Koutrouli, M., Karatzas, E., Paez-Espino, D. and Pavlopoulos, G.A., 2020. A guide to conquer the biological network era using graph theory. Frontiers in bioengineering and biotechnology, 8, p.34. 36. Lee, A. and Archambault, D., 2016, May. Communities found by users--Not algorithms: Comparing human and algorithmically generated communities. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 2396-2400). 37. Leskovec, J. and Mcauley, J., 2012. Learning to discover social circles in ego networks. Advances in neural information processing systems, 25. 38. Liu, S. and De Floriani, L., 2015. Multivariate Network Exploration and Presentations. Computer, 48(8), pp.6-6. 39. Luo, D., Li, C., Zhou, C. and Xing, J., 2020, November. On the Knowledge Graphs of Postgraduate Entrance English Examination Based on WordNet and D3. js. In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 1, pp. 991-996). IEEE. 40. MacEachren, A.M., Jaiswal, A., Robinson, A.C., Pezanowski, S., Savelyev, A., Mitra, P., Zhang, X. and Blanford, J., 2011, October. Senseplace2: Geotwitter analytics support for situational awareness. In 2011 IEEE conference on visual analytics science and technology (VAST) (pp. 181-190). IEEE. 41. Majeed, S., Uzair, M., Qamar, U. and Farooq, A., 2020, November. Social Network Analysis Visualization Tools: A Comparative Review. In 2020 IEEE 23rd International Multitopic Conference (INMIC) (pp. 1-6). IEEE. 42. Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S.A., Montesano, N., Tariq, M.I., De-la-Hoz-Franco, E. and De-La-Hoz-Valdiris, E., 2022. Trends and future perspective challenges in big data. In Advances in Intelligent Data Analysis and Applications: Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, 15–18 October 2019, Arad, Romania (pp. 309-325). Springer Singapore. 43. Odoni, F., Semar, W. and Mastrandrea, E., Visualisation of Collaboration in Social Collaborative Knowledge Management Systems. In Understanding Information Spaces. Proceedings of the 15th International Symposium of Information Science (ISI 2017) (p. 386). 44. Rafi, M., 2022. Social Media Network Analysis on Twitter Users Network to the Pension Plan Policy. Communicare: Journal of Communication Studies, 9(1), pp.62-76. 31 45. Romano, S., Capece, N., Erra, U., Scanniello, G. and Lanza, M., 2019. The city metaphor in software visualization: feelings, emotions, and thinking. Multimedia Tools and Applications, 78, pp.33113-33149. 46. Rusnak, V. and Drasar, M., 2023. Towards a Visual Analytics Workflow for Cybersecurity Simulations. 47. Sánchez, D.L., Revuelta, J., De la Prieta, F., Gil-González, A.B. and Dang, C., 2016. Twitter user clustering based on their preferences and the Louvain algorithm. In Trends in Practical Applications of Scalable Multi Agent Systems, the PAAMS Collection (pp. 349-356). Springer International Publishing. 48. Shao, L., Schleicher, T., Behrisch, M., Schreck, T., Sipiran, I. and Keim, D.A., 2016. Guiding the exploration of scatter plot data using motif-based interest measures. Journal of Visual Languages & Computing, 36, pp.1-12. 49. Sun, L., Dong, S., Ge, Y., Fonseca, J.P., Robinson, Z.T., Mysore, K.S. and Mehta, P., 2019. DiVenn: an interactive and integrated web-based visualization tool for comparing gene lists. Frontiers in Genetics, p.421. 50. Traag, V.A., Waltman, L. and Van Eck, N.J. (2019) ‘From Louvain to Leiden: guaranteeing well-connected communities’, Scientific Reports, 9(1), p. 5233. Available at: https://doi.org/10.1038/s41598-019-41695-z. 51. Understanding Community Detection Algorithms With Python NetworkX (2021). Available at: https://memgraph.com/blog/community-detection-algorithms-with-python-networkx (Accessed: 7 August 2023). 52. Xin, R., Ai, T. and Ai, B., 2018. Metaphor representation and analysis of non-spatial data in map-like visualizations. ISPRS International Journal of Geo-Information, 7(6), p.225. 53. Yang, C. and Liu, T., 2022. Social media data in urban design and landscape research: A Comprehensive literature review. Land, 11(10), p.1796. 54. Zafarani, R., Tang, L. and Liu, H., 2015. User identification across social media. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(2), pp.1-30. 55. Zhang, J., Huang, M.L., Wang, W.B., Lu, L.F. and Meng, Z.P., 2014, December. Big data density analytics using parallel coordinate visualization. In 2014 IEEE 17th International Conference on Computational Science and Engineering (pp. 1115-1120). IEEE. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14154/70025 | |
| dc.language.iso | en | |
| dc.publisher | Saudi Digital Library | |
| dc.subject | Data visualization | |
| dc.subject | network analysis | |
| dc.subject | interactive elements | |
| dc.subject | social behavior analysis | |
| dc.subject | JavaScript | |
| dc.title | Interactive Visualization and Analysis Dashboard for Social Media Networks and Multivariate Data | |
| dc.type | Thesis | |
| sdl.degree.department | Advance Computer Sciences | |
| sdl.degree.discipline | Data Sciences and Visualization | |
| sdl.degree.grantor | Newcatle University | |
| sdl.degree.name | Master's Degree |