Retweeting Behavior Prediction Based on One-Class Collaborative Filtering in Social Networks

Jiang, Bo; Liang, Jinsong; Sha, Ying; Li, Rui; Liu, Wei; Ma, Hongyuan; Wang, Lihong

doi:10.1145/2911451.2914713

Cited by 31 publications

(16 citation statements)

References 7 publications

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“…Likewise, Jiang et al [41] analyzed the fundamental factors that affect a concept called "re-tweetability" for each tweet when using a predictive filter for the collaboration between users [42], the connections, and the repetition of keywords in tweets. Therefore, Textual Analysis can be used to determine and identify the keywords with the greatest weight in a given sample and study the influence of these on the content.…”

Section: Textual Analysismentioning

confidence: 99%

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

2019

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The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.

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Section: Textual Analysismentioning

confidence: 99%

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

2019

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“…This method was based on Sentiment Analysis. The model extracts characteristics from the text of the comments made and implicitly captures them using standard ranges of the Gaussian field, as other authors have done [ 50 , 51 , 52 , 53 ].…”

Section: Related Workmentioning

confidence: 99%

Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An Exploratory Study

Saura

Palos-Sánchez

Martín

2018

IJERPH

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The object of this exploratory study is to identify the positive, neutral and negative environment factors that affect users who visit Spanish hotels in order to help the hotel managers decide how to improve the quality of the services provided. To carry out the research a Sentiment Analysis was initially performed, grouping the sample of tweets (n = 14459) according to the feelings shown and then a textual analysis was used to identify the key environment factors in these feelings using the qualitative analysis software Nvivo (QSR International, Melbourne, Australia). The results of the exploratory study present the key environment factors that affect the users experience when visiting hotels in Spain, such as actions that support local traditions and products, the maintenance of rural areas respecting the local environment and nature, or respecting air quality in the areas where hotels have facilities and offer services. The conclusions of the research can help hotels improve their services and the impact on the environment, as well as improving the visitors experience based on the positive, neutral and negative environment factors which the visitors themselves identified.

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“…Moreover, they have found and included among the best features the following ones: number of times the user is listed by other users, number of followers, and the average number of tweets posted per day. On the same line, Jiang et al [30] and Zhang et al [62] have treated the retweeting behavior prediction as a binary classification problem, achieving an accuracy of 0.85 and 0.789 respectively. Liu et al [35] have proposed a two-phase model to predict how many times a tweet can be retweeted in Sina Weibo microblog.…”

Section: Related Workmentioning

confidence: 99%

Assessing the reTweet proneness of tweets: predictive models for retweeting

Nesi

Pantaleo

Paoli

et al. 2018

Multimed Tools Appl

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The problem of assessing the mechanisms underlying the phenomenon of virality of social network posts is of great value for many activities, such as advertising and viral marketing, influencing and promoting, early monitoring and emergency response. Among the several social networks, Twitter.com is one of the most effective in propagating information in real time, and the propagation effectiveness of a post (i.e., tweet) is related to the number of times the tweet has been retweeted. Different models have been proposed in the literature to understand the retweet proneness of a tweet (tendency or inclination of a tweet to be retweeted). In this paper, a further step is presented, thus several features extracted from Twitter data have been analyzed to create predictive models, with the aim of predicting the degree of retweeting of tweets (i.e., the number of retweets a given tweet may get). The main goal is to obtain indications about the probable number of retweets a tweet may obtain from the social network. In the paper, the usage of the classification trees with recursive partitioning procedure for prediction has been proposed and the obtained results have been compared, in terms of accuracy and processing time, with respect to other methods. The Twitter data employed for the proposed study have been collected by using the Twitter Vigilance study and research platform of DISIT Lab in the last 18 months. The work has been developed in the context of smart city projects of the European Commission RESOLUTE H2020, in which the

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Retweeting Behavior Prediction Based on One-Class Collaborative Filtering in Social Networks

Cited by 31 publications

References 7 publications

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector: An Exploratory Study

Assessing the reTweet proneness of tweets: predictive models for retweeting

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