Call for Book Chapters: Deep Learning for Social Media Data Analytics

Deep Learning for Social Media Data Analytics (smda21) is an edited book (multi-authored book) to be published at Studies in Big Data, Springer book series (Approved). Social networking platforms are overwhelmed by different contents like photos, videos or texts shared by billions of users. These huge volumes of data generated are mostly unstructured, which has enormous potential to influence business, politics, security, planning and other social aspects. Mostly, social media posts are in the form of texts, or text along with images or video. Natural language processing plays an important role in analyzing those texts and drawing useful conclusions, which are necessary for sentiment analysis or opinion mining. Users often upload photos without captions, which may be personal or related to other things. Thus, recognition of images or the generation of captions is quite helpful in drawing valuable information about the photo or posts containing photos. Nowadays, a major security related challenge in social media platforms is detection of fake news or rumors or misinformation. To tackle this issue multi-modal analysis of data involving text, image and video is often necessary/useful. Furthermore, deep learning is used immensely for analyzing text, images and videos. This type of information has a number of possible applications in very diverse fields. This book covers various applications of deep learning techniques in social media to solve different problems.

Editors  

• Prof. Dr. Tzung-Pei Hong, Kaohsiung University, Kaohsiung, Taiwan (Email: tphong@nuk.edu.tw)
• Dr. Leticia Serrano Estrada, University of Alicante, Alicante, Spain (Email: leticia.serrano@ua.es)
• Dr. Akrati Saxena, Eindhoven University of Technology, Eindhoven, Netherlands (Email: a.saxena@tue.nl)
• Dr. Anupam Biswas, National Institute of Technology Silchar, India (Email: anupam@cse.nits.ac.in)

Topics

Deep learning techniques used in following topics of social media data analyics:

• Community Detection
• Link Prediction
• Sentiment Analysis
• Recommender System
• Opinion Mining
• Review Mining
• Hate Speech
• Rumor Detection
• Fake News Detection
• Social Media Security
• Image Recognition for Social Media Marketing
• Chatbots for Social Media Marketing
• Social Media Advertising
• Detection of Social Media Influencer
• Identification of Biased Information in Social Media
• Custom Topic Analysis for News Media
• Social Media Monitoring Agents
• Social Media for Effective Customer Service
• Mental Health Prediction/Analysis
• Anomalous user detection
• Spatiotemporal urban data analytics
• Social media data for urban planning
• Not limited to......

Submission Guidelines

The follow the following instructions:

• All chapters must be original and not simultaneously submitted to another book, journal or conference.
• Chapters cannot exceed similarity index of 10% excluding references.
• Chapter length 15-20 pages. Additional 5 more pages may be allowed on request.
• Chapters should be formated using Springer overleaf template https://www.overleaf.com/latex/templates/springer-book-chapter/hrdcrfynnzjn
• The review process is single blind. Therefore, author names and affiliations must be included. Do not write professional title, like Dr., Prof., etc.

Publication

Deep Learning for Social Media Data Analytics (SMDA 2021) to be published in Springer Book Series "Studies in Big Data".

NO PUBLICATION FEE

Contact

All questions about submissions should be emailed to anupam@cse.nits.ac.in