Abstract
During the crisis, people post a large number of informative and non-informative tweets on Twitter. Informative tweets provide helpful information such as affected individuals, infrastructure damage, availability and resource requirements, etc. In contrast, non-informative tweets do not provide helpful information related to either humanitarian organizations or victims. Identifying informative tweets is a challenging task during the disaster. People often post images along with text on Twitter during the disaster. In addition to tweet text features, image features are also crucial for identifying informative tweets. However, existing methods use only text features but do not use image features to identify crisis-related tweets during the disaster. This paper proposes a novel approach by considering the image features along with the text features. It includes a text-based classification model, an image-based classification model and a late fusion. The text-based classification model uses the Convolutional Neural Network (CNN) and the Artificial Neural Network (ANN). CNN is used to extract text features from a tweet and the ANN is used to classify tweets based on extracted text features of CNN. The image-based classification model uses the fine-tuned VGG-16 architecture to extract the image features from the image and classify the image in a tweet. The output of the text-based classification model and the image-based classification model are combined using the late fusion technique to predict the tweet label. Extensive experiments are carried out on Twitter datasets of various crises, such as the Mexico earthquake, California Wildfires, etc., to demonstrate the effectiveness of the proposed method. The proposed method outperforms the state-of-the-art methods on various parameters to identify informative tweets during the disaster.
Similar content being viewed by others
References
Alam F, Imran M, Ofli F (2019) Crisisdps: Crisis data processing services. In: Proceedings of the 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM)
Alam F, Ofli F, Imran M (2018) Crisismmd: Multimodal twitter datasets from natural disasters. In: AAAI Conference on Web and Social Media (ICWSM), AAAI, AAAI, Stanford, California, USA
Alam F, Ofli F, Imran M (2019) Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of hurricanes Harvey, Irma, and Maria. Behaviour & Information Technology, pp 1–31
Alam F, Ofli F, Imran M (2020) Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of hurricanes harvey, irma, and maria. Behaviour & Information Technology 39(3):288–318
Basu M, Shandilya A, Khosla P, Ghosh K, Ghosh S (2019) Extracting resource needs and availabilities from microblogs for aiding post-disaster relief operations. IEEE Transactions on Computational Social Systems 6(3):604–618
Breiman L (2001) Random forests. Machine learning 45(1):5–32
Burel G, Alani H (2018) Crisis event extraction service (crees)-automatic detection and classification of crisis-related content on social media. In: Proc. of the 15th ISCRAM
Caragea C, Silvescu A, Tapia A H (2016) Identifying informative messages in disaster events using convolutional neural networks. In: International Conference on Information Systems for Crisis Response and Management, pp 137–147
Caragea C, Squicciarini A C, Stehle S, Neppalli K, Tapia A H (2014) Mapping moods: Geo-mapped sentiment analysis during hurricane sandy.. In: ISCRAM
Castillo C (2016) Big crisis data: social media in disasters and time-critical situations. Cambridge University Press
Chollet F, et al. (2015) Keras. https://keras.io
Dunning T (1993) Accurate methods for the statistics of surprise and coincidence. Computational Linguistics 19(1):61–74. https://www.aclweb.org/anthology/J93-1003
Enenkel M, Saenz S M, Dookie D S, Braman L, Obradovich N, Kryvasheyeu Y (2018) Social media data analysis and feedback for advanced disaster risk management. CoRR abs/1802.02631 arXiv:1802.02631
Fan C, Wu F, Mostafavi A (2020) A hybrid machine learning pipeline for automated mapping of events and locations from social media in disasters. IEEE Access
Freund Y, Schapire R E, et al. (1996) Experiments with a new boosting algorithm. In: Icml, Bari, Italy, vol. 96, pp 148–156
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision, pp 630–645. Springer
Houston J B, Hawthorne J, Perreault M F, Park E H, Goldstein Hode M, Halliwell M R, Turner McGowen S E, Davis R, Vaid S, McElderry J A, et al. (2015) Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters 39(1):1–22
Howard A G, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR abs/1704.04861, pp 1–9
Imran M, Castillo C, Diaz F, Vieweg S (2015) Processing social media messages in mass emergency: A survey. ACM Computing Surveys (CSUR) 47(4):67
Imran M, Castillo C, Lucas J, Meier P, Vieweg S (2014) Aidr: Artificial intelligence for disaster response. In: Proceedings of the 23rd International Conference on World Wide Web, pp 159–162. ACM
Imran M, Mitra P, Castillo C (2016) Twitter as a lifeline: Human-annotated twitter corpora for nlp of crisis-related messages. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). European Language Resources Association (ELRA), Paris, France, pp 1638–1643
Kim Y (2014) Convolutional neural networks for sentence classification
Kingma D P, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings
Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Kryvasheyeu Y, Chen H, Obradovich N, Moro E, Van Hentenryck P, Fowler J, Cebrian M (2016) Rapid assessment of disaster damage using social media activity. Science advances 2(3):e1500779
Luhn H P (1957) A statistical approach to mechanized encoding and searching of literary information. IBM Journal of research and development 1(4):309–317
Madichetty S, Sridevi M (2019) Detecting informative tweets during disaster using deep neural networks. In: 2019 11th International Conference on Communication Systems & Networks (COMSNETS), pp 709–713. IEEE
Madichetty S, Sridevi M (2019) Disaster damage assessment from the tweets using the combination of statistical features and informative words. Social Network Analysis and Mining 9(1):42
Madichetty S, et al. (2018) Re-ranking feature selection algorithm for detecting the availability and requirement of resources tweets during disaster. International Journal of Computational Intelligence & IoT, 1(2)
McCulloch W S, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 5(4):115–133
Mitchell J T, Thomas Deborah SK, Hill A A, Cutter S L (2000) Catastrophe in reel life versus real life: Perpetuating disaster myth through hollywood films. International Journal of Mass Emergencies and Disasters 18 (3):383–402
Nguyen D T, Al Mannai K A, Joty S, Sajjad H, Imran M, Mitra P (2017) Robust classification of crisis-related data on social networks using convolutional neural networks. In: Eleventh International AAAI Conference on Web and Social Media, pp 632–635
Nguyen D T, Ofli F, Imran M, Mitra P (2017) Damage assessment from social media imagery data during disasters. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp 569–576. ACM
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830
Pennington J, Socher R, Manning C D (2014) Glove: Global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP). http://www.aclweb.org/anthology/D14-1162, pp 1532–1543
Purohit H, Castillo C, Diaz F, Sheth A, Meier P (2014) Emergency-relief coordination on social media: Automatically matching resource requests and offers. First Monday, 19(1)
Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review 65(6):386
Rudra K, Ganguly N, Goyal P, Ghosh S (2018) Extracting and summarizing situational information from the twitter social media during disasters. ACM Transactions on the Web (TWEB) 12(3):17
Rudra K, Ghosh S, Ganguly N, Goyal P, Ghosh S (2015) Extracting situational information from microblogs during disaster events: a classification-summarization approach. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp 583–592. ACM
Rudra K, Sharma A, Ganguly N, Ghosh S (2016) Characterizing communal microblogs during disaster events. In: Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on, pp 96–99. IEEE
Rudra K, Sharma A, Ganguly N, Imran M (2017) Classifying information from microblogs during epidemics. In: Proceedings of the 2017 International Conference on Digital Health, pp 104–108. ACM
Sarter N B, Woods D D (1991) Situation awareness: A critical but ill-defined phenomenon. Int J Aviat Psychol 1(1):45–57
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd international conference on learning representations, ICLR, pp 1–14
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929–1958
Vapnik V (2013) The nature of statistical learning theory. Springer science & business media
Vieweg S, Castillo C, Imran M (2014) Integrating social media communications into the rapid assessment of sudden onset disasters. In: International Conference on Social Informatics, vol 8851. Springer, pp 444–461
Werbos P (1974) Beyond regression: New tools for prediction and analysis in the behavior science. Unpublished Doctoral Dissertation, Harvard University
Zeiler M D (2012) Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Madichetty, S., M, S. Classifying informative and non-informative tweets from the twitter by adapting image features during disaster. Multimed Tools Appl 79, 28901–28923 (2020). https://doi.org/10.1007/s11042-020-09343-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09343-1