Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning

Amitani, Reishi; Matsumoto, Kazuyuki; Yoshida, Minoru; Kita, Kiyoshi

doi:10.3390/app112210567

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…The text features of the tweets were extracted by using the pre-trained BERT model, and the image features were obtained from pre-trained models such as VGG16. The results of the experiments showed that the correct response rate for predicting buzz classes with the proposed method using both text and image features was higher than when using the text or image features alone [36].…”

Section: Retweet Prediction Based On Heterogeneous Data Sourcesmentioning

confidence: 94%

“…In addition to retweet prediction, heterogeneous feature sources have also been successfully used to predict buzz tweets. Amitani et al [36], in their study on the classification of "buzz" tweets, examine the trends on social media and propose a classification method to study the factors that cause the buzz phenomenon on Twitter. This phenomenon can be understood as an explosion of popularity within a short period of time.…”

Section: Retweet Prediction Based On Heterogeneous Data Sourcesmentioning

confidence: 99%

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

2022

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Retweet prediction is an important task in the context of various problems, such as information spreading analysis, automatic fake news detection, social media monitoring, etc. In this study, we explore retweet prediction based on heterogeneous data sources. In order to classify a tweet according to the number of retweets, we combine features extracted from the multilayer network and text. More specifically, we introduce a multilayer framework for 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. Next, we select a set of local network measures from each layer and construct a set of multilayer network features. We also adopt a BERT-based language model, namely Cro-CoV-cseBERT, to capture the high-level semantics and structure of tweets as a set of text features. We then trained six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category-embedding model, neural oblivious decision ensembles, and an attentive interpretable tabular learning model for the retweet-prediction task. We compared the performance of all six algorithms in three different setups: with text features only, with multilayer network features only, and with both feature sets. We evaluated all the setups in terms of standard evaluation measures. For this task, we first prepared an empirical dataset of 199,431 tweets in Croatian posted between 1 January 2020 and 31 May 2021. Our results indicate that the prediction model performs better by integrating multilayer network features with text features than by using only one set of features.

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Section: Retweet Prediction Based On Heterogeneous Data Sourcesmentioning

confidence: 94%

Section: Retweet Prediction Based On Heterogeneous Data Sourcesmentioning

confidence: 99%

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

2022

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“…The text features of the tweets were extracted using the pre-trained BERT model, and the image features were obtained from pre-trained models such as VGG16. The results of the experiments showed that the correct response rate for predicting buzz classes with the proposed method using both text and image features was higher than using the features alone [28].…”

Section: Retweet Prediction Based On Heterogeneous Data Sourcesmentioning

confidence: 96%

“…In addition to retweets, heterogeneous feature sources have also been successfully used to predict buzz tweets. Amitani et al in [28], in their study on the classification of "buzz" tweets, examine the trends in social media and propose a classification method to study the factors that cause the buzz phenomenon on Twitter. This phenomenon can be understood as an explosion of popularity within a short period of time.…”

Section: Retweet Prediction Based On Heterogeneous Data Sourcesmentioning

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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“…Amitani et al [4] constructed a model to discriminate buzz tweets from text with images posted on Twitter using multi-task learning. Their method is similar to the present study in that it uses only the information at the time of tweet posting.…”

Section: Introductionmentioning

confidence: 99%

Trend Prediction Based on Multi-Modal Affective Analysis from Social Networking Posts

et al. 2022

Self Cite

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This paper propose a method to predict the stage of buzz-trend generation by analyzing the emotional information posted on social networking services for multimodal information, such as posted text and attached images, based on the content of the posts. The proposed method can analyze the diffusion scale from various angles, using only the information at the time of posting, when predicting in advance and the information of time error, when used for posterior analysis. Specifically, tweets and reply tweets were converted into vectors using the BERT general-purpose language model that was trained in advance, and the attached images were converted into feature vectors using a trained neural network model for image recognition. In addition, to analyze the emotional information of the posted content, we used a proprietary emotional analysis model to estimate emotions from tweets, reply tweets, and image features, which were then added to the input as emotional features. The results of the evaluation experiments showed that the proposed method, which added linguistic features (BERT vectors) and image features to tweets, achieved higher performance than the method using only a single feature. Although we could not observe the effectiveness of the emotional features, the more emotions a tweet and its reply match had, the more empathy action occurred and the larger the like and RT values tended to be, which could ultimately increase the likelihood of a tweet going viral.

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Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning

Cited by 4 publications

References 26 publications

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

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

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

Trend Prediction Based on Multi-Modal Affective Analysis from Social Networking Posts

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