A Multimodal Approach to Predict Social Media Popularity

Meghawat, Mayank; Yadav, Satyendra Singh; Mahata, Debanjan; Yin, Yifang; Shah, Rajiv Ratn; Zimmermann, Roger

doi:10.1109/mipr.2018.00042

Cited by 36 publications

(26 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On a similar direction there has been work on understanding the main intentions behind vulgar expressions in social media (Holgate et al, 2018). Various approaches have been taken to tackle both textual as well as multimodal data from Twitter and social media in general, in order to build deep learning classifiers for similar tasks (Baghel et al, 2018;Kapoor et al, 2018;Mahata et al, 2018a,b;Jangid et al, 2018;Meghawat et al, 2018;Shah and Zimmermann, 2017). The dataset provided for the tasks was collected through Twitter API by searching for tweets containing certain selected keyword patterns popular in offensive posts.…”

Section: Related Workmentioning

confidence: 99%

MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

Mahata¹,

Zhang²,

Uppal³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

Self Cite

View full text Add to dashboard Cite

In this paper, we present our approach and the system description for Sub-task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. Sub-task A involves identifying if a given tweet is offensive or not, and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individual). Our models for Sub-task A is based on an ensemble of Convolutional Neural Network, Bidirectional LSTM with attention, and Bidirectional LSTM + Bidirectional GRU, whereas for Sub-task B, we rely on a set of heuristics derived from the training data and manual observation. We provide a detailed analysis of the results obtained using the trained models. Our team ranked 5th out of 103 participants in Sub-task A, achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants in Sub Task B achieving a macro F1 of 0.695.

show abstract

Section: Related Workmentioning

confidence: 99%

MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

Mahata¹,

Zhang²,

Uppal³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the future, this work can be extended by exploiting multi-modalities in the data in the form of images, videos, and hyperlinks. Multi-modal approaches have extensively been used for various tasks like predicting social media popularity (Meghawat et al, 2018;Shah and Zimmermann, 2017). Another interesting aspect would be to adapt the pipeline described in this paper to different problems like identifying mentions of personal intake of medicine in social media (Mahata et al, 2018b,a).…”

Section: Resultsmentioning

confidence: 99%

Untitled

Mishra

Sinha

Sawhney

et al. 2019

Proceedings of the 2019 Conference of the North

Self Cite

View full text Add to dashboard Cite

Suicide is a leading cause of death among youth, and the use of social media to detect suicidal ideation is an active line of research. While it has been established that these users share a common set of properties, the current state-of-the-art approaches utilize only textbased (stylistic and semantic) cues. We contend that the use of information from networks in the form of condensed social graph embeddings and author profiling using features from historical data can be combined with an existing set of features to improve the performance. To that end, we experiment on a manually annotated dataset of tweets created using a threephase strategy and propose SNAP-BATNET, a deep learning based model to extract textbased features and a novel Feature Stacking approach to combine other community-based information such as historical author profiling and graph embeddings that outperform the current state-of-the-art. We conduct a comprehensive quantitative analysis with baselines, both generic and specific, that presents the case for SNAP-BATNET, along with an error analysis that highlights the limitations and challenges faced paving the way to the future of AI-based suicide ideation detection.

show abstract

“…In this regard, Wu et al [31] proposed a new deep learning framework to investigate the sequential prediction of image popularity by integrating temporal context and attention at different time scales. Moreover, Meghawat et al [32] developed an approach that integrates multiple multimodal information into a CNN model for predicting the popularity of images on Flickr. Although these studies have achieved satisfactory performances, they are not sufficiently powerful to capture and model the characteristics of image popularity.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media

Abousaleh

Cheng

et al. 2021

IEEE Trans. Cogn. Dev. Syst.

View full text Add to dashboard Cite

Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman's Rho, mean absolute error, and mean squared error, respectively.

show abstract

A Multimodal Approach to Predict Social Media Popularity

Cited by 36 publications

References 21 publications

MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

MIDAS at SemEval-2019 Task 6: Identifying Offensive Posts and Targeted Offense from Twitter

Untitled

Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media

Contact Info

Product

Resources

About