A Prediction Method of Peak Time Popularity Based on Twitter Hashtags

Abstract

Understanding the peak time of popularity evolution can provide insights on recommendation systems and online advertising campaigns. Although popularity evolution has been largely studied, the problem of how to predict its peak time remains unexplored. Taking Twitter hashtags as case study, the goal of this study is to predict when popularity reaches the peak for Twitter hashtags, from the perspective of an online social network application, in the context of the Twitter social network. On the whole, this paper includes three research aspects. Firstly, this paper investigates how early popularity reaches its peaks. Then, it is found that popularity tends to peak in the early stage of its evolution. Secondly, this paper discusses when a peak time prediction should be triggered. Thirdly, this paper designs a multi-modal based deep learning method, where the state-of-art deep learning techniques, such as multi-modal embedding and attention mechanisms, are adopted. We find that in the early stage of popularity evolution, no matter which factor is used as the input, the prediction effect is poor. By contrast, the hashtag string factor has the weakest contribution to popularity prediction in the middle and late stages of popularity evolution. The overall performance of our proposed method is evaluated in terms of the minimum, quartiles, and maximum values of absolute errors. From the experimental results, the prediction method we designed is superior.

Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

“…e recommendation list is output by Top-N, and the accuracy, recall, and F-measure is used to evaluate the recommendation quality of the experiment. To more obviously show the effectiveness of the collaborative filtering ad recommendation algorithm with labels proposed in this paper, the weight reconciliation factors α, β, and c are adjusted to 1 respectively, that is, the user-based collaborative ad recommendation algorithm [3], the label-based ad recommendation algorithm [15], and the label-item relationship-based ad recommendation algorithm [16] are obtained. To compare the recommendation quality of the above three algorithms and the proposed collaborative filtering ad recommendation algorithm with labels, three sets of experiments are designed in this paper: recommendation quality comparison of each algorithm for TOP5 recommendation, recommendation quality comparison of each algorithm with different N values, and recommendation quality optimization degree comparison.…”

“…e advertising industry has gradually developed from targeted delivery, where the value of ad delivery can be precisely measured, with a user-friendly and advertiser-beneficial advertising market [2]. Search engine revenue (Revenue Per Search, RPS) is one of the important evaluation indicators of the success of the search advertising recommendation system, which can be reflected by the pricing method of search advertising (such as CPC, Cost Per Click) and the ability of ads to attract users to click (Click-roughRate, CTR); that is, RPS � CTR × CPC [3]. erefore, it is important to predict CTR accurately and use it reasonably for ad recommendation.…”

Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment

“…The goal of the present study is to extend past research which has demonstrated the prominence of positive sentiment towards reopening, though there exists a fair amount of negative sentiment as well [8]. Though Twitter data has been intrinsically analyzed extensively and also contextualized to numerous domains, yet past research has not combined recent Twitter data with demographic data to model potential relationships between sentiment classes based on Tweets and COVID-19 relevant data [15][16][17]. To provide a meaningful research basis for such an exercise, the present study conducted focused literature review on relevant topics, as summarized in the following sections on Twitter analytics, human behavior and sentiment analysis.…”

Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment

“…The goal of the present study is to extend past research which has demonstrated the prominence of positive sentiment towards reopening, though there exists a fair amount of negative sentiment as well [8]. Though Twitter data has been intrinsically analyzed extensively and also contextualized to numerous domains, yet past research has not combined recent Twitter data with demographic data to model potential relationships between sentiment classes based on Tweets and COVID-19 relevant data [15–17]. To provide a meaningful research basis for such an exercise, the present study conducted focused literature review on relevant topics, as summarized in the following sections on Twitter analytics, human behavior and sentiment analysis.…”