Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory

Dipankar Das; Subhabrata Dutta; Tanmoy Chakraborty

REVIEW 4 major objections 4 minor 33 references

Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory

T0 review · 4 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read An Einstein-field-equation model predicts which user groups engage next in an online discussion and how fast it grows.

desk verdict New temporal engagement task with a plausible embedding, but the reported numbers are suspect due to a possible train/test leak in the user embedding and a cherry-picked headline. read the letter →

arxiv 1908.03770 v1 pith:Y6ETJTOX submitted 2019-08-10 cs.SI cs.LG

Subhabrata Dutta , Dipankar Das , Tanmoy Chakraborty This is my paper · ORCID

classification cs.SIcs.LG

keywords onlinediscussionuserengagementpredictiongrowthrateforecastingrelativisticgravityEinsteinfieldequationsembeddingGUVecRGNet

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the time-varying pattern of who comments next in an online discussion, and how quickly the discussion grows, can be predicted by treating the discussion as a cloud of 'dust' moving over a manifold of users and applying Einstein's field equations as the model of attraction. The authors propose GUVec, an embedding that places users near each other when they reply, comment at similar times, or engage with semantically similar posts, and RGNet, a neural network that computes a stress-energy tensor and a metric tensor from the discussion state and cluster centers, then reads off cluster engagement probabilities and a growth velocity from the resulting curvature. On the Reddit CMV corpus, RGNet reports 0.72 Micro F1 for temporal user-cluster engagement and a 6.01% average error for growth-rate forecasting, beating four baselines; on the non-temporal task of whether a post attracts any comment, it reports 0.62 AUC. The intended significance is that engagement is inherently dynamic, and a gravity-like fusion of content and user-history features can capture that dynamics in one model.

What carries the argument

The central machinery is the Einstein field equation reduced to a learnable tensor equation: $\sum_{j,k} g^{jk}(C_i) M_{jk}(C_i, T_i) = R'(C_i)$, where $g^{jk}$ is an inverse metric tensor computed from user-cluster centers $C_i$, $M_{jk}$ is a stress-energy tensor computed from discussion features and cluster positions, and $R'$ is a scalar curvature. RGNet learns diagonal approximations of $g$ and $M$ through feed-forward neural layers, then computes cluster-wise curvature as a product-sum $R'[l] = \sum_j M[i][l][j]\cdot g^{\mathrm{inv}}[i][l][j]$, which feeds a sigmoid for engagement probability and a ReLU for growth velocity. GUVec supplies the manifold: a GloVe-style objective over a user co-occurrence matrix built from communicative, temporal, and semantic proximity, producing fixed-dimensional user vectors that are clustered and prepended with time to form the spacetime manifold.

What would settle it

Rebuild the GUVec embeddings using only discussions up to May 7, 2015 (the training cutoff), retrain RGNet on the same training split, and evaluate on the May 8 to September 1, 2015 test split; if the Micro F1 falls from 0.72 toward the 0.64 LSTM baseline, the original gap is at least partly an artifact of test-period leakage.

Watch

Extended reading notes

Core claim

The paper claims that an online discussion can be modeled as a cloud of dust hovering over a spacetime manifold of users, where the Einstein field equations govern how the discussion attracts users from different clusters over time. In this model, a stress-energy tensor is learned from the post, comment windows, and user cluster positions; an inverse metric tensor is learned from cluster centers; and their elementwise product gives a scalar curvature for each cluster. That curvature is then mapped to two outputs: a probability that each user cluster will engage in the next comment window, and a nonnegative growth velocity for the discussion. The authors report that RGNet achieves 0.72 Micro F1 for temporal user-cluster engagement prediction and 6.01% average error for growth-rate forecasting on the Reddit CMV dataset, outperforming LSTM variants, a Newtonian-gravity model, and a logistic regression baseline; for non-temporal engagement prediction, it achieves 0.62 AUC, an 8.77% improvement over the existing baseline.

Load-bearing premise

The user embeddings that feed every experiment are computed from a co-occurrence matrix built over all users without an explicit statement that test-period discussions were excluded, so the reported temporal predictions may have relied on information from the future.

Editorial extensions

If this is right

If RGNet is right, it gives a working answer to a problem that had no previous solution: predicting, during an ongoing discussion, which user clusters will engage in the next comment window and at what rate the discussion will grow.
The reported 0.72 Micro F1 and roughly 6% growth error suggest that a single model can replace static post-level engagement classifiers with window-by-window forecasts, enabling live moderation, recommendation, or content promotion based on predicted engagement.
The feature-ablation results indicate that user-history (GUVec) features are the most important for both tasks, implying that temporal engagement is driven less by post text than by who the participants are and how they are connected.
The relativistic formulation provides a generic way to fuse heterogeneous features (text, surface, user vectors) into a joint curvature signal, and the non-temporal variant shows the same machinery transfers to the standard post-level engagement prediction task.
The model's performance degrades gracefully as the number of user clusters increases, and its growth-rate error decreases with more clusters, suggesting that finer-grained user partitioning improves the curvature estimate.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because the paper does not state that the user co-occurrence matrix A is built only from training-period discussions, the GUVec embeddings may encode test-period interactions; the true performance of a leak-free pipeline is an open empirical question, and the reported margin over baselines should be re-checked under that constraint.
The Einstein-field-equation vocabulary is used as an abstract approximation: the learned model reduces to feed-forward layers computing a diagonal product-sum, so a simpler bilinear or factorization model might achieve similar performance, making the gravitational framing a design choice rather than a proved necessity.
The analogy suggests testable extensions, such as learning positive curvature to represent user repulsion (the paper explicitly sets repulsion aside) or applying the same dust-cloud model to other platforms with timestamps and reply trees, like Twitter or StackOverflow.
The metric-distance analysis in the case study implies that the learned manifold stretches more for clusters that are about to engage, which could be used as a separate, unsupervised signal for engagement prediction beyond the supervised curvature output.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a real attempt at a new task—temporal engagement of user clusters—and the embedding plus multi-task architecture is reasonable. But two things keep me from trusting the numbers as reported: the user embedding may have been fit on test-period comments, and the abstract's headline F1 and error come from different cluster counts.

What's new and good: the task formulation (predict which clusters engage in the next window and how fast the discussion grows) is new relative to the static engagement work they cite. GUVec's combination of communicative, temporal, and semantic proximity is a sensible extension of GloVe to users, and the feature ablation in Table VI isolates the user feature's importance. RGNet's equations reduce to a feed-forward network with nonlinear layers, which is fine; the authors themselves call the relativity an 'abstract approximation.' Compared to the LSTM and Newtonian baselines, the reported margins are large.

Soft spots, in proportion. The big one is the leakage risk. Section II defines the co-occurrence matrix A over 'the entire set of such users' and never states that A is restricted to the pre-May-8-2015 training discussions. Section VI-A describes the train/test split for the dataset, but that section comes after the embedding definition, and the non-temporal task (Section V) doesn't state any split for GUVec either. Since Table VI shows the user feature group is the single most important input—removing it drops Micro F1 from 0.72 to 0.54—a leaky embedding could alone produce the reported gains. This is a documentation omission, not proof of cheating, but it has to be fixed with an explicit statement or code before the empirical claim is verifiable. The second issue is the headline metrics: Table IV(a) shows 0.72 Micro F1 at n=8 but 11.02% error, while 6.01% error only appears at n=32 with F1=0.44. The abstract cherry-picks the best of each. Third, there are no error bars, no repeated runs, and no significance tests despite the phrase 'outperforming... significantly.' Those are standard for this kind of paper, but they matter given the leakage risk.

Who it's for: people working on engagement prediction and social dynamics would get value from the task formulation and ablation, provided the leakage question is resolved. The physics analogy is not going to convince a physicist, and it doesn't need to; it's an interpretative frame, not a derivation.

Recommendation: send to peer review, but with a strongly worded request to address the embedding split, provide code/data, and report a single configuration with variance. If those checks come out clean, the paper could be a solid contribution. As it stands, it's conditional.

Referee Report

4 major / 4 minor

Summary. The paper introduces two temporal engagement tasks for online discussions—predicting which user clusters will comment in the next comment window, and forecasting the growth rate of a discussion—plus a non-temporal engagement task. It proposes GUVec, a GloVe-style user embedding built from communicative, temporal, and semantic co-occurrence, and RGNet, a feed-forward architecture whose intermediate quantities are named after the Einstein Field Equations (stress-energy tensor, inverse metric, Ricci scalar). Experiments on Reddit CMV and r/news data report that RGNet outperforms LSTM, Newtonian, and logistic-regression baselines on multi-label F1, growth-rate error, and AUC, with additional feature-ablation and complexity analyses.

Significance. If the empirical findings are valid, the paper makes a useful contribution: it defines a concrete temporal prediction setup, provides a user embedding tailored to co-commenting behavior, and evaluates it against several baselines with feature-ablation and complexity analyses. I credit the authors for the breadth of experiments, the explicit case study, and the complexity plot. However, the significance is currently conditional: the central results rest on a train/test protocol that is not stated precisely enough to exclude leakage, and the reported margins are not accompanied by variance or significance estimates. In addition, the physics vocabulary in RGNet is a naming layer rather than a constraint, so the theoretical novelty claim should be recalibrated. The paper does not provide code or data, which compounds the verification problem.

major comments (4)

[Secs. II and VI-A] In Sec. II, GUVec's co-occurrence matrix A is defined over 'the entire set of such users' from 'a discussion corpus,' with no statement that this corpus is restricted to the Jan 1, 2013–May 7, 2015 training discussions introduced in Sec. VI-A. The GUVec vectors and the cluster centers C_l, used in Eqs. (14)-(15) and as user features in Sec. III-C, are therefore potentially computed from test-period comments. The temporal label for window i+1 is exactly the set of clusters that comment in that window, and Table VI shows that removing user features drops Micro F1 from 0.72 to 0.54, so a leaky embedding is a high-impact channel. The authors must state explicitly that A, the GUVec training, and the cluster centers are built only from training discussions, and must rerun the experiments if this was not the case.
[Secs. V and VI-A] For the non-temporal task, Secs. V and VI-A describe 43,343 r/news posts collected over Sep 2016–Jan 2019 but do not specify any temporal or other split for constructing GUVec or for training RGNet. Because the task is to predict whether a post already in the collection attracts comments, using the full collection to build user embeddings can encode the outcome. Please state the split and ensure that GUVec, cluster centers, and RGNet are trained only on pre-split training posts.
[Sec. III-F, Eqs. (14)-(18)] The abstract and introduction claim that RGNet 'uses Einstein Field Equations of gravity to model online discussions,' and Eq. (11) is presented as a reduced EFE. However, M[i][l] in Eq. (14) and ginv[i][l] in Eq. (15) are outputs of learned MLPs with no constraint linking them to a metric, and R'[l] in Eq. (16) is simply their dot product; y1 and y2 are then nonlinear functions of these learned scalars. Training fits W1...W8 and B1...B6 to the labels, so the EFE imposes no structural constraint on the predictions; mathematically the model is a feed-forward network with physics-inspired names. Sec. III-B acknowledges this in passing ('only analogous' and 'abstract approximation'), but the abstract and introduction make a stronger claim. Please state precisely what, if anything, Eq. (11) constrains, or revise the framing so that the contribution is the GUVec embedding and the RGNet architecture rather than the use of EFE.
[Tables III-IV and Sec. VII] The abstract says RGNet 'outperforms all the baselines significantly,' but no error bars, confidence intervals, repeated-seed results, or significance tests are reported. In Table IV(a), at n=8 RGNet's growth-rate error (11.02) is close to LSTM-f's (11.23), and at n=16 the difference is much larger; single runs cannot establish significance. Please report means and standard deviations over multiple runs and apply paired tests (e.g., McNemar for classification, paired resampling for regression), or otherwise justify the word 'significantly.' Without this, the empirical central claim is not fully verifiable.

minor comments (4)

[Abstract] The abstract contains the typo 'Relativistic Gravitational Nerwork'; it should read 'Network.'
[Eq. (13)] The denominator in Eq. (13) is written as \sum_{j=0}^{i} w_j, but w_j has not been defined; presumably the intention is to sum the weights W3[j] or to use a different index. Please clarify.
[Sec. VII-A, Fig. 6(a)] In the discussion of Fig. 6(a), the text says 'as H_i increases (users already engaged tend to be members of same cluster),' but high entropy means the engaged users are spread across clusters rather than concentrated in one cluster; the parenthetical should be corrected.
[Sec. VII-A, Table IV(a)] The sentence about average degradation across cluster sizes is numerically inconsistent: for Micro F1 the consecutive degradation averages about 15.2% for RGNet and about 15.1% for LSTM-f, so RGNet is not the minimum; the value '10.15%' in the parenthetical is unexplained. Please revise the statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the EFE vocabulary is an explicit analogy, RGNet is a trained neural network, and no prediction reduces by construction to a fitted label or self-citation.

full rationale

The claimed derivation chain is not circular. GUVec (Sec. II, Eq. 4) is an unsupervised matrix-factorization embedding; RGNet (Sec. III, Eqs. 12-19) is a feed-forward network whose outputs y1 = sigmoid(R') and y2 = relu(Rtotal) are nonlinear functions of learned tensors M and ginv, with weights and biases fitted by BCE/MSE losses on training windows. Nothing in Eqs. 16-18 makes the test label an algebraic identity with the input: labels enter only through the loss, and evaluation is on held-out windows. The paper explicitly disclaims that the Einstein equations provide an independent physical constraint: 'Einstein's Field Equations are only abstract approximation learnt by RGNet. An exact mathematical model of engagement is far more complex, if not intractable' (Sec. III-B). There are no load-bearing self-citations and no uniqueness claim imported from the authors. The separate concern that GUVec may be constructed over the full corpus including test-period comments (Sec. II says 'Given the entire set of such users' without restricting A to the training split of Sec. VI-A) is a potential data-leakage and reproducibility issue, but it is not a circularity by the paper's own equations, so it does not raise the circularity score.

Assumptions & free parameters 6 free parameters · 5 assumptions · 2 invented entities

The account of engagement rests on several domain assumptions: attraction-only interaction, a learnable spacetime-like manifold, a diagonal dust stress-energy tensor, and cluster centers as manifold locations. The main fitted quantities are the GUVec vectors and the RGNet weights, plus hyperparameters d, w, n, and theta_0. No genuinely new physical entity is introduced; the spacetime manifold and dust cloud are metaphorical modeling constructs without external evidence.

free parameters (6)

GUVec user vectors and biases = Learned by minimizing Eq. 4 over the co-occurrence matrix A
These vectors define the user manifold on which all engagement predictions rest; they are fitted to the co-occurrence data.
RGNet weights W1-W8 and biases B1-B6 = Learned on the training set via Adam
Every tensor component in Eqs. 14-17 is a neural-network output, and the final curvatures and predictions are functions of these learned parameters.
Embedding dimension d = 128
Selected from {16, 32, 64, 128, 192, 256} using validation performance in Fig. 5(b); predictions depend on this choice.
Window size w = 15
Selected from {5, 10, 15, 20} by performance in Table IV(b); it defines the temporal prediction horizon.
Number of user clusters n = 8 for temporal, 24 for non-temporal
K-means k is a hand-chosen hyperparameter tuned per task; Tables IV(a) and V show results vary considerably with n.
Semantic proximity threshold theta_0 = pi/12
Chosen in Sec. VI-B primarily to limit computational cost; it controls which discussion-title pairs contribute to semantic proximity in GUVec.

assumptions (5)

domain assumption Engagement with a discussion is always non-negative attraction, never repulsion.
Sec. I states repulsion cannot be modeled without cognitive data or dislike features, so interaction is modeled as gravity-like attraction only. This shapes the positivity structure of y1 and y2.
domain assumption A user embedding with an appended time coordinate behaves like a (d+1)-dimensional spacetime manifold with a metric.
Sec. III-B says the time value is prepended to each user vector to convert it into a (d+1)-dimensional spacetime manifold. No geometric axioms are verified.
domain assumption A discussion can be represented as a dust cloud whose stress-energy tensor is diagonal.
Sec. III-A notes that for a cloud of dust only diagonal elements are non-zero, and Sec. III-D computes M as a vector representing these diagonal elements, without empirical support.
ad hoc to paper The Ricci scalar can be approximated as the dot product of learned stress-energy and inverse-metric vectors.
Eq. 16 defines R' as a sum of products of neural-network outputs; this is the central bridge between general relativity and engagement, and it is constructed for this paper rather than derived from differential geometry.
domain assumption K-means clusters of users capture meaningful groups whose centers define manifold regions.
Sec. II and Sec. VI-B use K-means cluster centers as the locations where curvature is evaluated; no cluster-quality or stability analysis is reported.

invented entities (2)

Discussion spacetime manifold
purpose: Mathematical space in which users are points and discussions curve the geometry; used to define cluster-wise curvature R'.
The paper posits this manifold by analogy, and no measurable prediction outside the model depends on it.
Discussion dust cloud stress-energy tensor M
purpose: Represents the discussion's attracting mass-energy at each cluster and window.
M is a learned neural-network output, not a measured quantity; it is defined so that Eq. 16 can produce engagement scores.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory." pith.science (2026). https://pith.science/paper/Y6ETJTOX

@misc{pith2026190803770,
  author       = {Pith},
  title        = {Pith review of: Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y6ETJTOX}},
  note         = {Machine review of arXiv:1908.03770}
}

read the original abstract

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 inter-related 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 Nerwork), 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.

Figures

Figures reproduced from arXiv: 1908.03770 by the authors.

**Figure 2.** Architecture of RGNet: Cj signifies j th comment (chronologically ordered by time) in the discussion. Colored arrows represent different transformations as defined in Eq. 12 to Eq. 18. electromagnetic or nuclear forces), rather an intrinsic property of spacetime, defined as curvature in M. Any object without the effect of any force, will follow a geodesic (a curve for which directional co-variant derivative along t… view at source ↗

**Figure 3.** Components of the stress-energy tensor. The components in blue, [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Architecture of the LSTM-f and LSTM-r models. LSTM layer in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: (a) Selection of θ0, (b) variation of RGNet’s performance w.r.t. d, (c) performance of user embedding methods for both the temporal tasks with RGNet. TABLE II EVALUATION METRICS USED FOR MULTI-LABEL CLASSIFICATION. THERE ARE n MULTI-LABEL INSTANCES (xi, yi) WITH yi BEI…

**Figure 6.** Figure 6: (a) Accuracy of RGNet with the change in homogeneity (measured by entropy), and (b) per-window error with increasing growth rate. TABLE V EVALUATION RESULTS FOR PREDICTING NON-TEMPORAL ENGAGEMENT. RGNE T-n SIGNIFIES RGNE T WITH n NUMBER OF CLUSTERS. Model F1-score AUC …

**Figure 7.** Figure 7: (Color online) Plot showing the number of non-zero elements in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

**Figure 8.** Figure 8: (Color online) An example prediction of user clusters by [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

33 extracted references · 29 canonical work pages

[1]

The utility of so- cial and topical factors in anticipating repliers in twitter conversations,

J. Schantl, R. Kaiser, C. Wagner, and M. Strohmaier, “The utility of so- cial and topical factors in anticipating repliers in twitter conversations,” in ACM WebSci. ACM, 2013, pp. 376–385

work page 2013
[2]

Who will reply to/retweet this tweet?: The dynamics of intimacy from online social interactions,

N. J. Yuan, Y . Zhong, F. Zhang, X. Xie, C.-Y . Lin, and Y . Rui, “Who will reply to/retweet this tweet?: The dynamics of intimacy from online social interactions,” in WSDM. ACM, 2016, pp. 3–12

work page 2016
[3]

Mining and comparing engagement dynamics across multiple social media platforms,

M. Rowe and H. Alani, “Mining and comparing engagement dynamics across multiple social media platforms,” in ACM WebSci. ACM, 2014, pp. 229–238

work page 2014
[4]

Post language and user engagement in online content communities,

V . Noguti, “Post language and user engagement in online content communities,” European Journal of Marketing , vol. 50, no. 5/6, pp. 695–723, 2016

work page 2016
[5]

Lettre de m, le verrier `a m: Faye sur la th ´eorie de mercure et sur le mouvement du p ´erih´elie de cette plan `ete,

U. Le Verrier, “Lettre de m, le verrier `a m: Faye sur la th ´eorie de mercure et sur le mouvement du p ´erih´elie de cette plan `ete,” Comptes rendus hebdomadaires des s ´eances de lAcad ´emie des sciences , vol. 49, pp. 379–383, 1859

work page
[6]

On the deﬂection of a light ray from its rectilinear motion, by the attraction of a celestial body at which it nearly passes by,

J. Soldner, “On the deﬂection of a light ray from its rectilinear motion, by the attraction of a celestial body at which it nearly passes by,” Berliner Astronomisches Jahrbuch, pp. 161–172, 1804

work page
[7]

Die feldgleichungen der gravitation,

A. Einstein, “Die feldgleichungen der gravitation,” Sitzung der physikalische-mathematischen Klasse, vol. 25, pp. 844–847, 1915

work page 1915
[8]

Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions,

C. Tan, V . Niculae, C. Danescu-Niculescu-Mizil, and L. Lee, “Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions,” in WWW, 2016, pp. 613–624

work page 2016

Show all 33 references

[9]

An ensemble method to produce high-quality word embeddings,

R. Speer and J. Chin, “An ensemble method to produce high-quality word embeddings,” arXiv preprint arXiv:1604.01692 , 2016

2016 arXiv
[10]

Glove: Global vectors for word representation,

J. Pennington, R. Socher, and C. Manning, “Glove: Global vectors for word representation,” in EMNLP, 2014, pp. 1532–1543

2014
[11]

A relation between distance and radial velocity among extra- galactic nebulae,

E. Hubble, “A relation between distance and radial velocity among extra- galactic nebulae,” PNAS, vol. 15, no. 3, pp. 168–173, 1929

1929
[12]

Readability of newspapers in 11 languages,

C.-H. Bj ¨ornsson, “Readability of newspapers in 11 languages,” Reading Research Quarterly, pp. 480–497, 1983

1983
[13]

Senticnet 5: Discov- ering conceptual primitives for sentiment analysis by means of context embeddings,

E. Cambria, S. Poria, D. Hazarika, and K. Kwok, “Senticnet 5: Discov- ering conceptual primitives for sentiment analysis by means of context embeddings,” in AAAI, 2018, pp. 1795–1802

2018
[14]

Adam: A method for stochastic optimization,

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

2014 arXiv
[15]

Multi-label classiﬁcation based on multi-objective optimization,

C. Shi, X. Kong, D. Fu, P. S. Yu, and B. Wu, “Multi-label classiﬁcation based on multi-objective optimization,” ACM TIST , vol. 5, no. 2, pp. 35:1–35:22, Apr. 2014

2014
[16]

Node2vec: Scalable feature learning for networks,

A. Grover and J. Leskovec, “Node2vec: Scalable feature learning for networks,” in SIGKDD, 2016, pp. 855–864

2016
[17]

Distributed representations of sentences and documents,

Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” in ICML, 2014, pp. 1188–1196

2014
[18]

Retweet prediction with attention-based deep neural network,

Q. Zhang, Y . Gong, J. Wu, H. Huang, and X. Huang, “Retweet prediction with attention-based deep neural network,” in CIKM, 2016, pp. 75–84

2016
[19]

Can cascades be predicted?

J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec, “Can cascades be predicted?” in WWW. ACM, 2014, pp. 925–936

2014
[20]

C-rbfnn: A user retweet behavior prediction method for hotspot topics based on improved rbf neural network,

Y . Liu, J. Zhao, and Y . Xiao, “C-rbfnn: A user retweet behavior prediction method for hotspot topics based on improved rbf neural network,” Neurocomputing, vol. 275, pp. 733–746, 2018

2018
[21]

Retweet wars: Tweet popularity prediction via dynamic multimodal regression,

K. Wang, M. Bansal, and J.-M. Frahm, “Retweet wars: Tweet popularity prediction via dynamic multimodal regression,” in WACV. IEEE, 2018, pp. 1842–1851

2018
[22]

Attentional image retweet modeling via multi-faceted ranking network learning

Z. Zhao, L. Meng, J. Xiao, M. Yang, F. Wu, D. Cai, X. He, and Y . Zhuang, “Attentional image retweet modeling via multi-faceted ranking network learning.” in IJCAI, 2018, pp. 3184–3190

2018
[23]

Reply trees in twitter: data analysis and branching process models,

R. Nishi, T. Takaguchi, K. Oka, T. Maehara, M. Toyoda, K.-i. Kawarabayashi, and N. Masuda, “Reply trees in twitter: data analysis and branching process models,” Social Network Analysis and Mining , vol. 6, no. 1, pp. 1–13, 2016

2016
[24]

Measuring user inﬂuence in twitter: The million follower fallacy,

M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummadi, “Measuring user inﬂuence in twitter: The million follower fallacy,” in ICWSM, 2010, pp. 10–17

2010
[25]

Why do people retweet? anti- homophily wins the day!

S. A. Macskassy and M. Michelson, “Why do people retweet? anti- homophily wins the day!” in ICWSM, 2011, pp. 209–216

2011
[26]

Characterization of the twitter@ replies network: are user ties social or topical?

D. Sousa, L. Sarmento, and E. Mendes Rodrigues, “Characterization of the twitter@ replies network: are user ties social or topical?” in Proceedings of the 2nd international workshop on Search and mining user-generated contents. ACM, 2010, pp. 63–70

2010
[27]

Everyone’s an inﬂuencer: quantifying inﬂuence on twitter,

E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, “Everyone’s an inﬂuencer: quantifying inﬂuence on twitter,” in WSDM. ACM, 2011, pp. 65–74

2011
[28]

The role of hidden inﬂuentials in the diffusion of online information cascades,

R. A. Ba ˜nos, J. Borge-Holthoefer, and Y . Moreno, “The role of hidden inﬂuentials in the diffusion of online information cascades,” EPJ Data Science, vol. 2, no. 1, pp. 1–16, 2013

2013
[29]

Reconstruction and analysis of twitter conversation graphs,

P. Cogan, M. Andrews, M. Bradonjic, W. S. Kennedy, A. Sala, and G. Tucci, “Reconstruction and analysis of twitter conversation graphs,” in Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research. ACM, 2012, pp. 25–31

2012
[30]

Under- standing user-community engagement by multi-faceted features: A case study on twitter,

H. Purohit, Y . Ruan, A. Joshi, S. Parthasarathy, and A. Sheth, “Under- standing user-community engagement by multi-faceted features: A case study on twitter,” inWWW 2011 Workshop on Social Media Engagement (SoME), 2011

2011
[31]

Scalable temporal latent space inference for link prediction in dynamic social networks,

L. Zhu, D. Guo, J. Yin, G. Ver Steeg, and A. Galstyan, “Scalable temporal latent space inference for link prediction in dynamic social networks,” IEEE TKDE, vol. 28, no. 10, pp. 2765–2777, 2016

2016
[32]

Deep reinforcement learning with a combinatorial action space for predicting popular reddit threads,

J. He, M. Ostendorf, X. He, J. Chen, J. Gao, L. Li, and L. Deng, “Deep reinforcement learning with a combinatorial action space for predicting popular reddit threads,” arXiv preprint arXiv:1606.03667 , 2016

2016 arXiv
[33]

Identifying the social signals that drive online discussions: A case study of reddit communities,

B. D. Horne, S. Adali, and S. Sikdar, “Identifying the social signals that drive online discussions: A case study of reddit communities,” in ICCCN. IEEE, 2017, pp. 1–9

2017

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

The utility of so- cial and topical factors in anticipating repliers in twitter conversations,

J. Schantl, R. Kaiser, C. Wagner, and M. Strohmaier, “The utility of so- cial and topical factors in anticipating repliers in twitter conversations,” in ACM WebSci. ACM, 2013, pp. 376–385

work page 2013

[2] [2]

Who will reply to/retweet this tweet?: The dynamics of intimacy from online social interactions,

N. J. Yuan, Y . Zhong, F. Zhang, X. Xie, C.-Y . Lin, and Y . Rui, “Who will reply to/retweet this tweet?: The dynamics of intimacy from online social interactions,” in WSDM. ACM, 2016, pp. 3–12

work page 2016

[3] [3]

Mining and comparing engagement dynamics across multiple social media platforms,

M. Rowe and H. Alani, “Mining and comparing engagement dynamics across multiple social media platforms,” in ACM WebSci. ACM, 2014, pp. 229–238

work page 2014

[4] [4]

Post language and user engagement in online content communities,

V . Noguti, “Post language and user engagement in online content communities,” European Journal of Marketing , vol. 50, no. 5/6, pp. 695–723, 2016

work page 2016

[5] [5]

Lettre de m, le verrier `a m: Faye sur la th ´eorie de mercure et sur le mouvement du p ´erih´elie de cette plan `ete,

U. Le Verrier, “Lettre de m, le verrier `a m: Faye sur la th ´eorie de mercure et sur le mouvement du p ´erih´elie de cette plan `ete,” Comptes rendus hebdomadaires des s ´eances de lAcad ´emie des sciences , vol. 49, pp. 379–383, 1859

work page

[6] [6]

On the deﬂection of a light ray from its rectilinear motion, by the attraction of a celestial body at which it nearly passes by,

J. Soldner, “On the deﬂection of a light ray from its rectilinear motion, by the attraction of a celestial body at which it nearly passes by,” Berliner Astronomisches Jahrbuch, pp. 161–172, 1804

work page

[7] [7]

Die feldgleichungen der gravitation,

A. Einstein, “Die feldgleichungen der gravitation,” Sitzung der physikalische-mathematischen Klasse, vol. 25, pp. 844–847, 1915

work page 1915

[8] [8]

Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions,

C. Tan, V . Niculae, C. Danescu-Niculescu-Mizil, and L. Lee, “Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions,” in WWW, 2016, pp. 613–624

work page 2016