Retweet Wars: Tweet Popularity Prediction via Dynamic Multimodal Regression

Wang, Ke; Bansal, Mohit; Frahm, Jan‐Michael

doi:10.1109/wacv.2018.00204

Cited by 30 publications

(26 citation statements)

References 23 publications

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“…Liu et al [20] proposed a user behavior model for retweet prediction. Recently, studies on the role of multimodality in retweet prediction have gained much focus [21], [22]. Another problem, which is much similar to ours, is the reply prediction [1]- [3], [23].…”

Section: E Performance Of Guvecmentioning

confidence: 82%

Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory

Dutta

Das

Chakraborty

2019

2019 IEEE International Conference on Data Mining (ICDM)

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Online discussions are valuable resources to study user behaviour on a diverse set of topics. Unlike previous studies which model a discussion in a static manner, in the present study, we model it as a time-varying process and solve two interrelated problems -predict which user groups will get engaged with an ongoing discussion, and forecast the growth rate of a discussion in terms of the number of comments. We propose RGNet (Relativistic Gravitational Network), a novel algorithm that uses Einstein Field Equations of gravity to model online discussions as 'cloud of dust' hovering over a user spacetime manifold, attracting users of different groups at different rates over time. We also propose GUVec, a global user embedding method for an online discussion, which is used by RGNet to predict temporal user engagement. RGNet leverages different textual and network-based features to learn the dust distribution for discussions.We employ four baselines -first two using LSTM architecture, third one using Newtonian model of gravity, and fourth one using a logistic regression adopted from a previous work on engagement prediction. Experiments on Reddit dataset show that RGNet achieves 0.72 Micro F1 score and 6.01% average error for temporal engagement prediction of user groups and growth rate forecasting, respectively, outperforming all the baselines significantly. We further employ RGNet to predict non-temporal engagement -whether users will comment to a given post or not. RGNet achieves 0.62 AUC for this task, outperforming existing baseline by 8.77% AUC. Post User cluster 1 User cluster 2 User cluster 3 User cluster 4 Comments Post • Communicative Proximity: If u i , u j replied to each other, then increment A ij by 2.

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Section: E Performance Of Guvecmentioning

confidence: 82%

Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory

Dutta

Das

Chakraborty

2019

2019 IEEE International Conference on Data Mining (ICDM)

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“…Table 5 shows the performance of our GBRT with RMSE objective and new feature representation. Features extracted from the quoted content did not pro- (Wang, Bansal, and Frahm 2018) vide a significant boost over SOTA, likely due to visual modality dominating in the T2016-IMG dataset, as considered by (Wang, Bansal, and Frahm 2018). The approach did however match the performance of (Kowalczyk and Larsen 2019) in virality ranking, and achieves strong (Cohen 1988) performance, without considering image modality.…”

Section: Metricsmentioning

confidence: 89%

“…Retroactive filtering of Twitter archive allows us to rebuild datasets used in prior work e.g. (Wang, Bansal, and Frahm 2018;Kowalczyk and Larsen 2019) and to reduce topic bias via near-uniform sampling across long time-frames (Figure 1). Collecting a dataset similar to T2017-ML by sampling Twitter Firehose popular in prior work, would have taken 14 months.…”

Section: Data Collectionmentioning

confidence: 99%

“…The abundance of information to which we are exposed through online social networks is exceeding our capacity to consume it (Weng et al 2012), let alone in a critical way. (Weng et al 2012;Qiu et al 2017) show that content quality is not a prerequisite for viral spreading, and (Lorenz-Spreen et al 2019) show that the competition for our attention is growing, causing individual topics to receive even shorter intervals of collective attention. Consequently, our exposure to low-quality information and by extension low-quality influence, is increasing (Table 1).…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, our exposure to low-quality information and by extension low-quality influence, is increasing (Table 1). Still, few studies systematically investigate how to model the strength of influence in heterogeneous information networks, and the processes that drive popularity in our limited-attention world remain mostly unexplored (Franck 2019;Weng et al 2012). Propagation metrics (retweet count in particular) do not capture, the average individual attention received.…”

Section: Introductionmentioning

confidence: 99%

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The Complexity of Social Media Response: Statistical Evidence for One-dimensional Engagement Signal in Twitter

Kowalczyk

Hansen²

2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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Many years after online social networks exceeded our collective attention, social influence is still built on attention capital. Quality is not a prerequisite for viral spreading, yet the size of diffusion cascades is still the hallmark of social influence. Consequently, our exposure to low-quality content and questionable influence is expected to increase. Since the conception of influence maximization frameworks, multiple content performance metrics became available, albeit raising the complexity of influence analysis. In this paper, we examine and consolidate a diverse set of content engagement metrics. The correlations discovered, lead us to propose a new compound engagement signal. We then show it is more predictable than any individual influence predictor investigated before. Our proposed model achieves strong engagement ranking performance and is the first to explain half of the variance with features available at the time of engagement. We share the detailed numerical workflow to compute the new compound engagement signal. The model is immediately applicable to social media monitoring, influencer identification, campaign engagement forecasting, and curating user feeds.

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