Deep fusion of multimodal features for social media retweet time prediction

Yin, Hui; Yang, Shuiqiao; Song, Xiangyu; Liu, Wei; Li, Jianxin

doi:10.1007/s11280-020-00850-7

Cited by 47 publications

(19 citation statements)

References 26 publications

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“…There have been many research reports on how to effectively use disaster-related tweets for situational awareness during emergencies and disasters [ 5 , 6 ]. Discovering informative content from social media platforms is an important task for government agencies and rescue organizations [ 2 , 41 ]. In this section, we give a brief overview of the work related to TABERT.…”

Section: Related Workmentioning

confidence: 99%

Identifying informative tweets during a pandemic via a topic-aware neural language model

Gao

Tao

et al. 2022

World Wide Web

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Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.

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Section: Related Workmentioning

confidence: 99%

Identifying informative tweets during a pandemic via a topic-aware neural language model

Gao

Tao

et al. 2022

World Wide Web

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“…The idea of redefining the Knowledge Tracing problem in terms of graphs has recently gained significant momentum due to the widespread deployment of GNNs [14,10,15,16,17] and breakthroughs in addressing the unpredictability of traditional approaches to crossconcept exercises. Traditional KT usually takes sequential data as input in the form of concepts corresponding to the input exercises and their responses.…”

Section: Introductionmentioning

confidence: 99%

Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

Song¹,

Li²,

Liu³

et al. 2022

Preprint

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The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises. The benefit of knowledge tracing is that students' learning plans can be better organised and adjusted, and interventions can be made when necessary. With the recent rise of deep learning, Deep Knowledge Tracing (DKT) has utilised Recurrent Neural Networks (RNNs) to accomplish this task with some success. Other works have attempted to introduce Graph Neural Networks (GNNs) and redefine the task accordingly to achieve significant improvements. However, these efforts suffer from at least one of the following drawbacks: 1) they pay too much attention to details of the nodes rather than to high-level semantic information; 2) they struggle to effectively establish spatial associations and complex structures of the nodes; and 3) they represent either concepts or exercises only, without integrating them. Inspired by recent advances in self-supervised learning, we propose a Bi-Graph Contrastive Learning based Knowledge Tracing (Bi-CLKT) to address these limitations. Specifically, we design a two-layer comparative learning scheme based on an "exercise-to-exercise" (E2E) relational subgraph. It involves node-level contrastive learning of subgraphs to obtain discriminative representations of exercises, and graph-level contrastive learning to obtain discriminative representations of concepts. Moreover, we designed a joint contrastive loss to obtain better representations and hence better prediction performance. Also, we explored two different variants, using RNN and memory-augmented neural networks as the prediction layer for comparison to obtain better representations of exercises and concepts respectively. Extensive experiments on four real-world datasets show that the proposed Bi-CLKT and its variants outperform other baseline models.

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“…Recently, especially for the monitoring and tracking different aspects of healthcare information and public disease [8,10,11]. Among all the user behavior in social media, the retweet is considered one of the primary functions for spreading information on Twitter [12,13]. There is a large number of studies that deal with the prediction of information spreading on Twitter and other social networks.…”

Section: Introductionmentioning

confidence: 99%

Retweet Prediction based on Heterogeneous Data Sources: The Combination of Text and Multilayer Network Features

Meštrović¹,

Petrović²,

Beliga³

2022

Preprint

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Retweet prediction is an important task related to different problems such as information spreading analysis, the automatic detection of fake news, social media monitoring, etc. In this study we explore the possibilities of retweet prediction based on heterogeneous data sources. In order to classify the tweet according to the amount of retweets, we combine features extracted from the multilayer network and the text. More specifically, we introduce a multilayer framework that proposes the multilayer network representation of Twitter. This formalism captures different users' actions and complex relationships as well as other key properties of communication on Twitter. We select a set of local network measures from each layer and construct a set of multilayer network features. In addition, we adopt a BERT-based language model, namely Cro-CoV-cseBERT to capture high-level semantics and structure of tweets as a set of text features. Then, we train six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category embedding model, neural oblivious decision ensembles and attentive interpretable tabular learning model in the task of retweet prediction. We compare the performance of all six algorithms in three different setups (i) using only text features, (ii) using only multilayer network features and (iii) using both sets of features. We evaluate all setups in terms of standard evaluation measures i.e. precision, recall, F1-score and accuracy. For this task, we first prepare and use an empirical dataset of 199,431 tweets in the Croatian language posted during the period between January 1, 2020 and May 31, 2021. Our results indicate that by integrating multilayer network features with text features the prediction model would perform better than using just one set of features.

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Deep fusion of multimodal features for social media retweet time prediction

Cited by 47 publications

References 26 publications

Identifying informative tweets during a pandemic via a topic-aware neural language model

Identifying informative tweets during a pandemic via a topic-aware neural language model

Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

Retweet Prediction based on Heterogeneous Data Sources: The Combination of Text and Multilayer Network Features

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