User emotion for modeling retweeting behaviors

Chen, Jinpeng; Liu, Yu; Zou, Ming

doi:10.1016/j.neunet.2017.08.006

Cited by 25 publications

(16 citation statements)

References 21 publications

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“…In accordance with previous research in Twitter, we considered retweets as a measure of users’ particular interest in a topic, which might be associated to the emotions elicited by the tweets in them [53]. In addition, we have explored the value of likes for the same purpose and found a moderate positive correlation between both indices.…”

Section: Discussionmentioning

confidence: 74%

Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets

Pereira-Sánchez¹,

Álvarez-Mon²,

Barco³

et al. 2019

J Med Internet Res

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Background Hikikomori is a severe form of social withdrawal, originally described in Japan but recently reported in other countries. Debate exists as to what extent hikikomori is viewed as a problem outside of the Japanese context. Objective We aimed to explore perceptions about hikikomori outside Japan by analyzing Western language content from the popular social media platform, Twitter. Methods We conducted a mixed methods analysis of all publicly available tweets using the hashtag #hikikomori between February 1 and August 16, 2018, in 5 Western languages (Catalan, English, French, Italian, and Spanish). Tweets were first classified as to whether they described hikikomori as a problem or a nonproblematic phenomenon. Tweets regarding hikikomori as a problem were then subclassified in terms of the type of problem (medical, social, or anecdotal) they referred to, and we marked if they referenced scientific publications or the presence of hikikomori in countries other than Japan. We also examined measures of interest in content related to hikikomori, including retweets, likes, and associated hashtags. Results A total of 1042 tweets used #hikikomori, and 656 (62.3%) were included in the content analysis. Most of the included tweets were written in English (44.20%) and Italian (34.16%), and a majority (56.70%) discussed hikikomori as a problem. Tweets referencing scientific publications (3.96%) and hikikomori as present in countries other than Japan (13.57%) were less common. Tweets mentioning hikikomori outside Japan were statistically more likely to be retweeted ( P =.01) and liked ( P =.01) than those not mentioning it, whereas tweets with explicit scientific references were statistically more retweeted ( P =.01) but not liked ( P =.10) than those without that reference. Retweet and like figures were not statistically significantly different among other categories and subcategories. The most associated hashtags included references to Japan, mental health, and the youth. Conclusions Hikikomori is a repeated word in non-Japanese Western languages on Twitter, suggesting the presence of hikikomori in countries outside Japan. Most tweets treat hikikomori as a problem, but the ways they post about it are highly heterogeneous.

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Section: Discussionmentioning

confidence: 74%

Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets

Pereira-Sánchez¹,

Álvarez-Mon²,

Barco³

et al. 2019

J Med Internet Res

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“…The evaluation methods of microblog influence include PageRank based on the number of followers (Kwak, Lee, Park, & Moon, 2010), the topic-sensitive method of TwitterRank (Weng, Lim, Jiang, & He, 2010), Twitter User Rank (TURank) based on user behavior (Yamaguchi, Takahashi, Amagasa, Kitagawa, & Kitagawa, 2010), the method based on the order relationship of forwarding (Bakshy, Hofman, Mason, & Watts, 2011), the InfluenceRank algorithm applied by constructing a regression model (Nargundkar & Rao, 2016), the WeiboRank algorithm from the comment dimension (Liao, Wang, Han, & Zhang, 2013), the personalized PageRank algorithm that considers the user's network topology characteristics (Alp & Öğüdücü, 2018), and the Learn-to-Rank algorithm that relates the user's behavior to the sentiment of published content (Chen, Liu, & Zou, 2017). Some researchers have tried to establish evaluation indicator systems for microblog influence, such as microblog influence evaluation system for major emergencies (Luo, 2013) and the evaluation system of information dissemination influence of individual microblog users (Xiao & Qi, 2013).…”

Section: Analysis Of the Key Factors Of Microblog Influencementioning

confidence: 99%

An Influence Prediction Model for Microblog Entries on Public Health Emergencies

Luo

Han

et al. 2019

Data and Information Management

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This study aims at constructing a microblog influence prediction model and revealing how the user, time, and content features of microblog entries about public health emergencies affect the influence of microblog entries. Microblog entries about the Ebola outbreak are selected as data sets. The BM25 latent Dirichlet allocation model (LDA-BM25) is used to extract topics from the microblog entries. A microblog influence prediction model is proposed by using the random forest method. Results reveal that the proposed model can predict the influence of microblog entries about public health emergencies with a precision rate reaching 88.8%. The individual features that play a role in the influence of microblog entries, as well as their influence tendencies are also analyzed. The proposed microblog influence prediction model consists of user, time, and content features. It makes up the deficiency that content features are often ignored by other microblog influence prediction models. The roles of the three features in the influence of microblog entries are also discussed.

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“…Therefore, some researchers analyze the factors that affect individual spreading behavior in social media, and then establish a variety of models to predict individual spreading behavior in social media. Chen et al propose a semi-supervised graph model (SGM) to predict the retweeting behavior by detecting users' emotional status corresponding with their current mood from their friends' tweets, then using Learn-to-Rank method, the Top-N retweets are obtained [10]. Ding and Tian build a model based on a back propagation neural network (BPNN) to predict the retweeting behavior of social media users, which extracts 11 feature vectors from recipient characteristics, retweeter characteristics, tweet content characteristics, and external media coverage [11].…”

Section: Introductionmentioning

confidence: 99%

Predicting Rumor Retweeting Behavior of Social Media Users in Public Emergencies

et al. 2020

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Rumors in social media not only affect the health of online social networks, but also reduce the quality of information accessed by social media users. When emergencies occur, the rapid spread of rumors can even trigger mass anxiety and panic. However, the existing studies did not make a clear distinction between rumor and non-rumor information in public emergencies, so that they cannot effectively predict the rumor retweeting behavior. To this end, a model for predicting rumor retweeting behavior is presented based on the convolutional neural networks (CNN) called R-CNN model in the paper. In this model, the rumor retweeting behavior is considered as an important driving force of increasing the depth and breadth of rumor cascades, and four feature vectors are constructed with the historical textual content published by users, consisting of attention to public emergencies, attention to rumors, reaction time and tweeting frequency. To input the quantitative feature vectors for R-CNN, a K-means based core tweets extraction method is proposed to select the right tweets, and the quantitative feature representations are proposed. The predictive capability of the model has been proved by experiments base on two rumor datasets of emergencies crawled from Sina weibo. Experimental results indicate that the prediction accuracy of the model reaches 88%, and it can be improved by 7% on average compared with other models. INDEX TERMS Behavior prediction, convolutional neural networks, public emergency, rumor.

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User emotion for modeling retweeting behaviors

Cited by 25 publications

References 21 publications

Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets

Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets

An Influence Prediction Model for Microblog Entries on Public Health Emergencies

Predicting Rumor Retweeting Behavior of Social Media Users in Public Emergencies

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