Machine Learning Approach to Analyze and Predict the Popularity of Tweets with Images

Joseph, Nimish; Sultan, Amir; Kar, Arpan Kumar; Ilavarasan, P. Vigneswara

doi:10.1007/978-3-030-02131-3_49

Cited by 4 publications

(7 citation statements)

References 17 publications

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“…The OLS experiment utilises this simple yet efficient regression approach for modelling the Social Influence index. The results obtained are after running the regression analysis (Joseph, Sultan,Kar, & Ilavarasan, 2018). It is evident from the statistics summary that the determination coefficient (R-Squared) is 0.894 which is close to 1.…”

Section: Ordinary Least Square (Ols) Resultsmentioning

confidence: 75%

“…Using the MLR techniques (Joseph, Sultan,Kar, & Ilavarasan, 2018), features those are having high association with social influence are identified. We had proposed the following three hypotheses,…”

Section: High Impact Features Identification Resultsmentioning

confidence: 99%

“…Therefore, there is a need to introduce a method which can be used to measure social influence of a celebrity based on how intimately they are able to engage users with their post. Various computing approaches such as Evolutionary algorithm (Agarwal & Mehta, 2018), Nature inspired algorithm (Aswani, Ghrera, , Kar, & Chandra, 2017) and machine learning approaches (Joseph, Sultan,Kar, & Ilavarasan, 2018) are used by researchers as learning mechanism on other social media issues. Classification models are also used to learn the tweets for predicting test data (Joseph, Sultan,Kar, & Ilavarasan, 2018).…”

Section: Theoretical Basis and Hypotheses Developmentmentioning

confidence: 99%

“…Various computing approaches such as Evolutionary algorithm (Agarwal & Mehta, 2018), Nature inspired algorithm (Aswani, Ghrera, , Kar, & Chandra, 2017) and machine learning approaches (Joseph, Sultan,Kar, & Ilavarasan, 2018) are used by researchers as learning mechanism on other social media issues. Classification models are also used to learn the tweets for predicting test data (Joseph, Sultan,Kar, & Ilavarasan, 2018). Henceforth, machine learning models had been used as the theoretical lens for framing the context of learning model in order to resolve social media content based influence measurement issue.…”

Section: Theoretical Basis and Hypotheses Developmentmentioning

confidence: 99%

See 3 more Smart Citations

Measuring social media influencer index- insights from facebook, Twitter and Instagram

Arora

Bansal²,

Kandpal³

et al. 2019

Journal of Retailing and Consumer Services

435

198

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The growth of social media has completely revamped the way people interact, communicate and engage. These platforms play a key role in facilitating greater outreach and influence. This study proposes a mechanism for measuring the influencer index across popular social media platforms including Facebook, Twitter, and Instagram. A set of features that determine the impact on the consumers are modelled using a regression approach. The underlying machine learning algorithms including Ordinary Least Squares (OLS), K-NN Regression (KNN), Support Vector Regression (SVR), and Lasso Regression models are adapted to compute a cumulative score in terms of influencer index. Findings indicate that engagement, outreach, sentiment, and growth play a key role in determining the influencers. Further, the ensemble of the four models resulted in the highest accuracy of 93.7% followed by the KNN regression with 93.6%. The study has implications across various domains of e-commerce, viral marketing, social media marketing and brand management wherein identification of key information propagators is essential. These influencer indices may further be utilized by e-commerce portals and brands for the purpose of social media promotion and engagement for larger outreach.

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Section: Ordinary Least Square (Ols) Resultsmentioning

confidence: 75%

Section: High Impact Features Identification Resultsmentioning

confidence: 99%

Section: Theoretical Basis and Hypotheses Developmentmentioning

confidence: 99%

Section: Theoretical Basis and Hypotheses Developmentmentioning

confidence: 99%

See 2 more Smart Citations

Measuring social media influencer index- insights from facebook, Twitter and Instagram

Arora

Bansal²,

Kandpal³

et al. 2019

Journal of Retailing and Consumer Services

435

198

View full text Add to dashboard Cite

show abstract

“…It provided better results than the nearest neighbour approach [27]. In [28], the popularity prediction of an image posted a tweet on Twitter via learning mechanisms like Neural Networks (NN), Random Forests (RF) and Decision Tree (DT). The historical variables of tweet and image parameters are identified and used to train the prediction of extracting tweeter image data.…”

Section: Introductionmentioning

confidence: 99%

Popularity Prediction of Social Media Post Using Tensor Factorization

Bohra¹,

Bhatnagar²,

Choudhary³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user's interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about how popular the post will be and addresses any anomalies caused by factors outside of the study. A novel improved PARAFAC (A-PARAFAC) method that is tensor factorization-based has been presented in order to cope with the user criteria that will be used in the future to rate any project. We consolidated the information on the historically popular content, and we accelerated the computation by choosing the top contents that were most like each other. The tensor is factorised with the application of the Adam optimization. It has been modified such that the bias is now included in the gradient function of A-PARAFAC, and the value of the bias is updated after each iteration. The prediction accuracy is improved by 32.25% with this strategy compared to other state of the art methods.

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The Half-Life of a Tweet

Matter¹,

Sargsyan²

2023

ICWSM

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Twitter has started to share an impression count variable as part of the available public metrics for every Tweet collected with Twitter’s APIs. With the information about how often a particular Tweet has been shown to Twitter users at the time of data collection, we can learn important insights about the dissemination process of a Tweet by measuring its impression count repeatedly over time. With our preliminary analysis, we can show that on average the peak of impressions per second is 72 seconds after a Tweet was sent and that after 24 hours, no relevant number of impressions can be observed for ∼95% of all Tweets. Finally, we estimate that the median half-life of a Tweet, i.e. the time it takes before half of all impressions are created, is about 80 minutes.

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Machine Learning Approach to Analyze and Predict the Popularity of Tweets with Images

Cited by 4 publications

References 17 publications

Measuring social media influencer index- insights from facebook, Twitter and Instagram

Measuring social media influencer index- insights from facebook, Twitter and Instagram

Popularity Prediction of Social Media Post Using Tensor Factorization

The Half-Life of a Tweet

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