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REVIEW 5 major objections 6 minor 77 references

The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing

T0 review · 5 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Rewriting a Reddit title when sharing a YouTube video measurably boosts engagement, and longer, lexically richer, less neutral, community-aware titles win in matched comparisons.

desk verdict A big, careful observational study whose title-effect conclusion is plausible but overstated: the 0.5-hour pairing window does not eliminate the timing confound, and re-ordering pairs by score lets timing masquerade as wording. read the letter →

arxiv 2505.03769 v1 pith:NMU34AUS submitted 2025-04-26 cs.SI cs.AIcs.IR

Yibo Hu , Yiqiao Jin , Meng Ye , Ajay Divakaran , Srijan Kumar This is my paper · ORCID
classification cs.SIcs.AIcs.IR
keywords cross-platformcontentsharinguserengagementtitlerewritingRedditYouTubepairwiserankingBERTconfoundingcontrol
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 claims that when Reddit users share a YouTube video, rewriting the video's title into a new post title measurably improves engagement, even after holding the video, the subreddit, and the posting time fixed. On a large dataset of Reddit posts sharing YouTube videos, it finds that 21% of titles are near-copies of the original video title, and that heavily rewritten titles are associated with higher scores. In a multi-phase matched-pair design, more popular posts consistently have longer, lexically richer, less neutral, and more community-specific titles. A fine-tuned BERT model predicts which of two titles got more engagement with about 74% accuracy, while GPT-4o and time- or view-based baselines stay near random, suggesting the signal is real but subtle. If right, the result means wording alone is a lever for engagement in cross-platform content sharing.

What carries the argument

The load-bearing object is the multi-phase matched-pair experiment. Posts are paired within the same subreddit: Exact Match pairs share the identical video, Similar Match pairs share videos of comparable view counts (view ratio 0.5–2), and Inverse Match pairs reverse the expected popularity–views relationship. Additional filters restrict pairs to a 0.5-hour posting window, require meaningful title difference via Levenshtein distance thresholds, and demand a doubled score gap. The paper then runs paired t-tests, Wilcoxon signed-rank tests, and McNemar tests on five feature families (structural, lexical, stylistic, readability, sentiment), and validates learnability with a pairwise BERT ranking model.

What would settle it

A matched-pair replication on a fresh sample of Reddit-YouTube posts that applies the same filters but finds no significant difference, or a reversed direction, in the headline title features (length, CTTR/MTLD lexical diversity, neutral-sentiment share) between more and less popular posts would falsify the central claim. More directly, if a randomized field experiment that assigns rewritten versus copied titles to identical videos, subreddits, accounts, and posting times showed no engagement advantage for rewritten titles, the claim would collapse.

Watch

Extended reading notes

Core claim

The central discovery is that title rewrites measurably improve engagement and that the improvement has a describable text profile. In matched pairs where subreddit, video identity or view similarity, and posting time are controlled, the more popular post typically has a longer title, richer vocabulary as measured by length-adjusted indices, stronger (less neutral) sentiment, and language that resonates with the subreddit's norms. The effect grows in the inverse-match condition, where the more popular post is linked to the less popular video, which the paper reads as evidence that text becomes decisive when video popularity cannot explain the outcome. The paper also shows that a context-aware model trained on these controlled pairs can rank titles by engagement at 74% accuracy, while general-purpose LLM evaluation is near chance, indicating the patterns are learnable but not trivial.

Load-bearing premise

Once subreddit, video identity or similar view counts, and a half-hour posting window are held fixed, the remaining popularity difference within each pair must be driven by the title text alone and not by the earlier post's extra exposure, the poster's identity, the thumbnail, or any other unobserved user choice.

Editorial extensions

If this is right

  • If wording alone shifts engagement, then low-cost title rewriting is a concrete optimization lever for anyone sharing video content across platforms.
  • Effective titles are not simply "simpler" or "shorter": the matched data point to informative, lexically rich, emotionally non-neutral titles, so content strategies should track these features.
  • Community norms gate the effect: a title that works in r/SquaredCircle or r/kpop is specific to that audience, so global "best title" rules will underperform subreddit-aware ones.
  • Because GPT-4o and simple baselines fail at the pairwise task while BERT succeeds, engagement prediction from titles is a learnable but non-obvious task, which implies automated title-recommendation systems may need fine-tuned models rather than general LLM prompts.
  • The matched-pair framework provides a template for isolating textual effects in other cross-platform settings, such as Twitter/X or TikTok sharing.

Reading between the lines

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

  • The causal read rests on unconfoundedness after matching; titles are user-chosen, not randomized, so hidden differences like poster identity, thumbnail choice, or the earlier post's extra exposure could partially explain the score gaps, and I would expect the true effect size to be smaller than the raw association.
  • A natural next test is to apply the same pairing design to other platform pairs (e.g., news headlines shared on Twitter/X, TikTok captions) to see whether the same title-feature profile holds or is Reddit-specific.
  • The large BERT-versus-GPT-4o gap suggests a concrete probe: adversarially edit titles in matched pairs (lengthen, de-neutralize, add community keywords) and check whether the model's predicted ranking flips in the same direction as the observed scores; that would test whether the model learned the paper's stated features or something correlated.
  • If the finding is robust, one testable extension is an actual A/B test on a platform that permits randomized headline assignment, comparing copied titles against community-matched rewrites with identical timing, video, and account.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper studies whether rewriting the titles of Reddit posts that share YouTube videos affects engagement. The authors build a large Reddit-YouTube dataset, report that 21% of titles are near-copies of video titles, and use a multi-phase matched-pair design (Exact, Similar, and Inverse Match) to compare titles of more- and less-popular posts. They find that more popular posts have longer, more lexically diverse, less neutral, and more community-specific titles. A fine-tuned BERT model predicts the more popular post in pairs with about 74% accuracy, outperforming random, time-based, view-based, and GPT-4o baselines. The paper interprets these results as evidence that title rewrites causally improve engagement and that the controlled dataset isolates text effects.

Significance. If the causal conclusion held, the paper would make a valuable contribution to cross-platform engagement research: it introduces a large dataset, a thoughtful matched-pair framework, and a plausible predictive demonstration that title text carries information about engagement within near-identical sharing contexts. The statistical work is careful in several respects — multiple tests, Bonferroni corrections, effect sizes, and robustness splits are reported. However, the central causal claim is not established by the design. The paper's own figure and baselines show residual timing effects, and the analysis reorders pairs by the outcome variable after pairing, so the reported feature differences and BERT accuracy can arise from exposure time or selection on the outcome rather than from title text. The contribution is still substantial as a descriptive and predictive study, but the manuscript overstates its causal status.

major comments (5)
  1. [§4.1–§4.2] The matched-pair design does not neutralize the earlier post's extra exposure. Figure 6 shows that in Exact Match pairs the later post wins only about 44% of the time after four hours, and the text states that a 0.5-hour window 'diminishes' but does not eliminate this bias. Section 4.2 then explicitly replaces submission-order assignment: 'post pairs are ordered by engagement, with Post 1 being the more popular (Score 1 > Score 2), replacing earlier timing- or random-based assignments.' Because the order is now determined by the outcome, any residual timing advantage will disproportionately assign the earlier post to Group 1. The additional score-ratio filter ('at least double the score') selects pairs in which this residual advantage materialized. As a result, all Group 1-versus-Group 2 feature comparisons and the BERT ranking can reflect posting time even if title text has no effect. The authors should condition on which post was submitted first or provide a sensitivity analysis across time windows and submission-order strata.
  2. [§4.4–§4.5] The feature comparisons are descriptive contrasts between posts selected on the outcome: Group 1 is defined by Score 1 > Score 2 after enforcing a doubling of score and a minimum score difference of 20. Under this design, any title feature that is even weakly correlated with engagement can show a significant pairwise difference, so the t-tests, Wilcoxon signed-rank tests, and McNemar tests do not establish that title rewrites cause engagement. The paper should either present this part as a correlational analysis or use an identification strategy that accounts for the selection rule, such as pairing equally on posting order and applying within-pair causal methods rather than post-hoc ordering by score.
  3. [§5.4, Table 7] The time-based ranking baseline achieves 56.8% on the Date split and 53.3% on average, versus roughly 50% for random guessing. The text interprets this as 'validating our timing controls,' but if the time window had removed timing effects, ranking by earlier submission should perform near chance. The above-chance time baseline is direct evidence that a timing signal remains in the curated dataset, which is especially problematic because Section 4.2 reorders pairs by engagement rather than by submission time. At minimum, the authors should report the time-based baseline on the exact feature-comparison dataset and discuss why a 6.8-point deviation from chance is compatible with the claim that timing is controlled.
  4. [Abstract and §4.1] The manuscript repeatedly describes the design as a 'controlled experiment,' but title assignment is observational; the authors do not randomize which title appears on the earlier versus later post. The Conclusion itself acknowledges that thumbnails, descriptions, and other multimodal elements are not considered, and these are exactly the kind of unobserved confounders that can co-vary with title rewriting. The causal claim that 'title rewrites measurably improve engagement' therefore rests on an unconfoundedness assumption that is neither tested nor convincingly defended. The language should be softened to 'associated with' or the authors should add an explicit sensitivity analysis for unobserved confounders.
  5. [§4.1 and §4.5, Figure 8] The Inverse Match phase selects pairs by outcome: it begins with cases where Score 1 > Score 2 and VVR1,2 ≤ 1, i.e., the more popular post links to a less popular video. This is a post-hoc selection on the outcome, not a controlled manipulation. The 'amplified text effects' in Figure 8 therefore may be an artifact of the selection rule: by requiring a large score contrast in a direction opposite to video popularity, the authors mechanically select pairs in which text features (or unobserved variables) happen to align with the score difference. The comparison of T-statistics between Similar and Inverse phases is not a valid demonstration that text effects become stronger when video influence is removed.
minor comments (6)
  1. [§4.1] The heading 'Inverse Match Phrase' should read 'Inverse Match Phase'; the same misspelling appears in Table 8's column header, where 'Phrase' should be 'Phase.'
  2. [§5.6.2 and Figure 9] The text refers to 'Pair H' and the caption lists pairs (A–H), but the figure displays only pairs A–G; either add Pair H or correct the caption and reference.
  3. [§5.3] The prediction experiments use a 'strict 0.1-hour time window,' whereas Section 4 uses a 0.5-hour window; the manuscript does not explain this discrepancy or report sensitivity of the prediction results to the window choice.
  4. [§5.2, Eq. (1)] The max-margin loss is written as Loss(x1, x2) = max(0, x2 − x1) after defining xi as the model's score; as written this is minimized when both scores are arbitrarily negative. Please clarify whether a margin term or an additional 'score the opposite pair' term is used, or define the loss with reversed scores.
  5. [§5.6.1] There is a typo in 'gaming subredditsl ike r/Games' — it should read 'gaming subreddits like r/Games.'
  6. [Conclusion] The sentence 'restricting the dataset to Reddit-YouTube interactions may limits the generalizability' contains a subject-verb agreement error ('may limits' should be 'may limit').

Circularity Check

0 steps flagged · score 1.0 of 10

No definitional or equation-level circularity; the only soft spot is an interpretive leap from BERT accuracy to dataset quality.

full rationale

The paper contains no equation-level circularity. The multi-phase matching procedure constructs post pairs by shared video, subreddit, and a short timing window; the outcome variable is the observed post score. Group 1 and Group 2 are defined by score differences, not by the title features being tested, so the reported feature differences (longer, lexically richer, less neutral titles) are empirical measurements rather than tautologies. The pairwise BERT ranking is trained on one split and evaluated on held-out date/post/video splits, and its ~74% accuracy is a genuine supervised result, not a fitted constant renamed as a prediction. Heuristic baselines such as random guessing and video-views ranking are evaluated on the same held-out regime. The only soft spot is the inferential leap from BERT accuracy to the abstract's claim that the results 'validate that our controlled dataset effectively minimizes confounding effects'; that is an interpretive claim about unobserved confounders, not a circular dependence in the derivation. Self-citations in the paper (e.g., [25], [32], [33], [35]-[38]) support methods and related work but are not load-bearing for the core novelty claim. The paper's own conclusion acknowledges residual limitations, including omitted thumbnails and ecosystem-wide dynamics, which are external-validity concerns rather than evidence of circularity.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The paper contributes no formal derivation; its claims rest on dataset construction choices, matching assumptions, and statistical conventions. The main burden is unconfoundedness after matching, which is asserted rather than tested, and several threshold choices directly shape the central result.

free parameters (6)
  • Pairwise Similarity Threshold (LD) = 70
    Pairs of titles with LD > 70 are removed to preserve variation; the choice is motivated by the dataset average LD of 63 and by an ablation on test accuracy, so it tunes the evaluation set.
  • Video-Title Replication Threshold (LD) = 95
    Titles with LD > 95 relative to the video title are excluded; this removes 21% of posts and raises BERT accuracy from 65% to 74%, so the central claim depends on this exclusion.
  • Minimum score contrast = 2x and >=20
    The more popular post must have at least double the score and a minimum absolute difference of 20, which selects pairs with large engagement gaps and affects all downstream statistics.
  • Posting time window = 0.5 hour; 0.1 hour for prediction
    Pairing within 30 minutes is justified by the Exact phase result that timing bias stabilizes; the prediction task uses a stricter 0.1-hour window. This is a design choice affecting the dataset.
  • Video view ratio range = 0.5 to 2
    Similar-phase pairs are restricted to undirected VVR max < 2 to control video popularity; this boundary is a design choice that determines which pairs enter the analysis.
  • Subreddit inclusion threshold = top 5,000; >=1,000 posts
    The analysis focuses on the largest communities to reduce noise from tiny subreddits, which affects the generalizability of the engagement patterns.
assumptions (5)
  • domain assumption Reddit post score is a valid and comparable measure of user engagement across pairs.
    Used throughout; scores are treated as comparable despite being collected at one time rather than time-aligned within each pair.
  • domain assumption Unconfoundedness given matched controls: after matching subreddit, video, views, and time, title differences are independent of other engagement drivers.
    Section 4.1; this is the load-bearing premise that makes the controlled-experiment interpretation valid, and it is untestable in observational data.
  • domain assumption YouTube video views and categories proxy video popularity sufficiently for matching.
    Used in Similar and Inverse phases; ignores thumbnails, channel-level effects, and the timing of view counts.
  • domain assumption Levenshtein distance and SBERT cosine similarity capture meaningful rewrite magnitude.
    Used to define title modification bins and exclusion thresholds; LD is character-level and may misclassify paraphrases or very different wording with similar meaning.
  • standard math The Central Limit Theorem justifies paired t-tests despite non-normal data.
    Section 4.4; D'Agostino tests show non-normality, and CLT is invoked because sample sizes are large.

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Cite this review

Pith. "Pith review of The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing." pith.science (2026). https://pith.science/paper/NMU34AUS

@misc{pith2026250503769,
  author       = {Pith},
  title        = {Pith review of: The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NMU34AUS}},
  note         = {Machine review of arXiv:2505.03769}
}
read the original abstract

In today's cross-platform social media landscape, understanding factors that drive engagement for multimodal content, especially text paired with visuals, remains complex. This study investigates how rewriting Reddit post titles adapted from YouTube video titles affects user engagement. First, we build and analyze a large dataset of Reddit posts sharing YouTube videos, revealing that 21% of post titles are minimally modified. Statistical analysis demonstrates that title rewrites measurably improve engagement. Second, we design a controlled, multi-phase experiment to rigorously isolate the effects of textual variations by neutralizing confounding factors like video popularity, timing, and community norms. Comprehensive statistical tests reveal that effective title rewrites tend to feature emotional resonance, lexical richness, and alignment with community-specific norms. Lastly, pairwise ranking prediction experiments using a fine-tuned BERT classifier achieves 74% accuracy, significantly outperforming near-random baselines, including GPT-4o. These results validate that our controlled dataset effectively minimizes confounding effects, allowing advanced models to both learn and demonstrate the impact of textual features on engagement. By bridging quantitative rigor with qualitative insights, this study uncovers engagement dynamics and offers a robust framework for future cross-platform, multimodal content strategies.

Figures

Figures reproduced from arXiv: 2505.03769 by the authors.

Figure 1
Figure 1. Example posts from r/videos, Reddit’s largest video-sharing subreddit. The ranking and visibility of posts are influenced by user votes, titles, video content, timing, and interaction dynamics. [2, 11, 17, 22, 31, 47, 65, 70, 73, 74, 77]. However, identifying the factors driving post success in this complex environment remains a significant challenge for researchers and practitioners [9, 26]. While prior research ha… view at source ↗
Figure 2
Figure 2. Word clouds illustrate the distinct thematic focuses of video posts in three large subreddits: [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Temporal variation in mean post scores by (A) hour of the day, (B) day of the week, and (C) month (smoothed over three-month [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Video Statistics: (A) Frequency distribution of YouTube video categories; (B) Log-scaled distribution of video views; (C) Positive [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Title modifications and engagement: (A) Degree of rewriting in post titles compared to their corresponding video titles, [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Time factor analysis in Exact Match Phase. Count and ratio of posts with higher scores across time windows, comparing Post [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Video popularity analysis in Similar Match Phase. Post engagement across Video View Ratio ( [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: T-statistics from paired t-tests across Similar (A) and Inverse (B) phrases for continuous metrics. Bars represent the magnitude and direction of the standardized mean differences, with colors denoting significance levels. The amplified trends in Inverse cases highligh…
Figure 9
Figure 9. Figure 9: Case studies of title effectiveness in post pairs: Post pairs (A–H) from the controlled dataset compare more popular (Post 1) [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]

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Works this paper leans on

77 extracted references · 57 canonical work pages

  1. [1]

    Behnoush Abdollahi, Mahsa Badami, Gopi Chand Nutakki, Wenlong Sun, and Olfa Nasraoui. 2014. A two step ranking solution for twitter user engagement. In Proceedings of the Recommender Systems Challenge (RecSys’14) . ACM, Foster City, California, USA, 35–40

    work page 2014

  2. [2]

    Adiya Abisheva, Venkata Rama Kiran Garimella, David Garcia, and Ingmar Weber. 2014. Who watches (and shares) what on youtube? and when? using twitter to understand youtube viewership. In Proceedings of the 7th ACM international conference on Web search and data mining . 593–602

    work page 2014

  3. [3]

    Kholoud Khalil Aldous, Jisun An, and Bernard J Jansen. 2019. View, like, comment, post: Analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 13. AAAI Press, Munich, Germany, 47–57

    work page 2019

  4. [4]

    Kholoud Khalil Aldous, Jisun An, and Bernard J Jansen. 2022. What really matters?: characterising and predicting user engagement of news postings using multiple platforms, sentiments and topics. Behaviour & Information Technology 0 (2022), 1–24

    work page 2022

  5. [5]

    Hélder Antunes and Carla Teixeira Lopes. 2019. Analyzing the adequacy of readability indicators to a non-English language. In Experimental IR Meets Multilinguality, Multimodality, and Interaction: 10th International Conference of the CLEF Association, CLEF 2019, Lugano, Switzerland, September 9–12, 2019, Proceedings 10 . Springer, 149–155

    work page 2019

  6. [6]

    Marco Arazzi, Marco Cotogni, Antonino Nocera, and Luca Virgili. 2023. Predicting Tweet Engagement with Graph Neural Networks. In Proceedings of the 2023 ACM International Conference on Multimedia Retrieval . 172–180

    work page 2023

  7. [7]

    Marco Arazzi, Serena Nicolazzo, Antonino Nocera, and Manuel Zippo. 2023. The importance of the language for the evolution of online communities: An analysis based on Twitter and Reddit. Expert Systems with Applications 222 (2023), 119847

    work page 2023

  8. [8]

    Stefano Baccianella, Andrea Esuli, Fabrizio Sebastiani, et al. 2010. Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining.. In Lrec, Vol. 10. Valletta, 2200–2204

    work page 2010

Show all 77 references
  1. [9]

    Eytan Bakshy, Jake Hofman, Winter Mason, and Duncan Watts. 2011. Everyone’s an Influencer: Quantifying Influence on Twitter. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, WSDM 2011 , 65–74. https://doi.org/10.1145/1935826.1935845

    2011

  2. [10]

    Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching Word Vectors with Subword Information. arXiv preprint arXiv:1607.04606 (2016)

    2016 arXiv

  3. [11]

    Cody Buntain, Richard Bonneau, Jonathan Nagler, and Joshua A Tucker. 2021. YouTube recommendations and effects on sharing across online social platforms. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–26

    2021

  4. [12]

    John B Carroll. 1964. Language and thought. Reading Improvement 2, 1 (1964), 80

    1964

  5. [13]

    Jilin Chen and Peter Pirolli. 2012. Why you are more engaged: factors influencing twitter engagement in occupy Wall Street. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’12) , Vol. 6(1). AAAI Press, Dublin, Ireland, 423–426

    2012

  6. [14]

    Michal Daniluk, Jacek Dabrowski, Barbara Rychalska, and Konrad Goluchowski. 2021. Synerise at RecSys 2021: Twitter user engagement prediction with a fast neural model. In Proceedings of the Recommender Systems Conference (RecSys’21) . ACM, Amsterdam, Netherlands, 15–21

    2021

  7. [15]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs/1810.04805 (2018). arXiv:1810.04805 http://arxiv.org/abs/1810.04805 Manuscript submitted to ACM 24 Yibo Hu, Yiqiao...

    2018 arXiv

  8. [16]

    Ernesto Diaz-Aviles, Hoang Thanh Lam, Fabio Pinelli, Stefano Braghin, Yiannis Gkoufas, Michele Berlingerio, and Francesco Calabrese. 2014. Predicting user engagement in Twitter with collaborative ranking. In Proceedings of the Recommender Systems Challenge (RecSys’14) . ACM, F...

    2014

  9. [17]

    Hao Fang, Hao Cheng, and Mari Ostendorf. 2016. Learning Latent Local Conversation Modes for Predicting Comment Endorsement in Online Discussions. In Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media . Association for Computational...

    2016 doi

  10. [18]

    James N Farr, James J Jenkins, and Donald G Paterson. 1951. Simplification of Flesch reading ease formula. Journal of applied psychology 35, 5 (1951), 333

    1951

  11. [19]

    Nicolò Felicioni, Andrea Donati, Luca Conterio, Luca Bartoccioni, Davide Yi Xian Hu, Cesare Bernardis, and Maurizio Ferrari Dacrema. 2020. Multi-objective blended ensemble for highly imbalanced sequence aware tweet engagement prediction. In Proceedings of the Recommender Syste...

    2020

  12. [20]

    Panagiotis Fytas, Georgios Rizos, and Lucia Specia. 2021. What makes a scientific paper be accepted for publication? arXiv preprint arXiv:2104.07112 (2021)

    2021 arXiv

  13. [21]

    Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023. ChatGPT outperforms crowd workers for text-annotation tasks.Proceedings of the National Academy of Sciences 120, 30 (2023), e2305016120

    2023

  14. [22]

    Dimitris C Gkikas, Katerina Tzafilkou, Prokopis K Theodoridis, Aristogiannis Garmpis, and Marios C Gkikas. 2022. How do text characteristics impact user engagement in social media posts: Modeling content readability, length, and hashtags number in Facebook. International Journ...

    2022

  15. [23]

    Robert Gunning. 1969. The fog index after twenty years. Journal of Business Communication 6, 2 (1969), 3–13

    1969

  16. [24]

    Xingzhi Guo, Brian Kondracki, Nick Nikiforakis, and Steven Skiena. 2022. Verba Volant, Scripta Volant: Understanding Post-publication Title Changes in News Outlets. In Proceedings of the ACM Web Conference 2022 . 588–598

    2022

  17. [25]

    Bing He, Yibo Hu, Yeon-Chang Lee, Soyoung Oh, Gaurav Verma, and Srijan Kumar. 2025. A survey on the role of crowds in combating online misinformation: Annotators, evaluators, and creators. ACM Transactions on Knowledge Discovery from Data 19, 1 (2025), 1–30

    2025

  18. [26]

    Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li, and Li Deng. 2016. Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Pro...

    2016 doi

  19. [27]

    Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173–182

    2017

  20. [28]

    Jack Hessel, Lillian Lee, and David M. Mimno. 2017. Cats and Captions vs. Creators and the Clock: Comparing Multimodal Content to Context in Predicting Relative Popularity. CoRR abs/1703.01725 (2017). arXiv:1703.01725 http://arxiv.org/abs/1703.01725

    2017 arXiv

  21. [29]

    S Hochreiter. 1997. Long Short-term Memory. Neural Computation MIT-Press (1997)

    1997

  22. [30]

    Liangjie Hong, Ovidiu Dan, and Brian D. Davison. 2011. Predicting Popular Messages in Twitter. In Proceedings of the 20th International Conference Companion on World Wide Web (Hyderabad, India) (WWW ’11) . Association for Computing Machinery, New York, NY, USA, 57–58. https: /...

    2011

  23. [31]

    Horne, Sibel Adali, and Sujoy Sikdar

    Benjamin D. Horne, Sibel Adali, and Sujoy Sikdar. 2017. Identifying the social signals that drive online discussions: A case study of Reddit communities. CoRR abs/1705.02673 (2017). arXiv:1705.02673 http://arxiv.org/abs/1705.02673

    2017 arXiv

  24. [32]

    Yibo Hu, MohammadSaleh Hosseini, Erick Skorupa Parolin, Javier Osorio, Latifur Khan, Patrick Brandt, and Vito D’Orazio. 2022. Conflibert: A pre-trained language model for political conflict and violence. Association for Computational Linguistics

    2022

  25. [33]

    Yibo Hu, Erick Skorupa Parolin, Latifur Khan, Patrick Brandt, Javier Osorio, and Vito D’Orazio. 2024. Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification. In Proceedings of the 62nd Annual Meeting of the Association for Computatio...

    2024

  26. [34]

    Clayton Hutto and Eric Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media , Vol. 8. 216–225

    2014

  27. [35]

    Yiqiao Jin, Yunsheng Bai, Yanqiao Zhu, Yizhou Sun, and Wei Wang. 2023. Code Recommendation for Open Source Software Developers. InWeb Conference

    2023

  28. [36]

    Yiqiao Jin, Mohit Chandra, Gaurav Verma, Yibo Hu, Munmun De Choudhury, and Srijan Kumar. 2024. Better to ask in English: Cross-lingual evaluation of large language models for healthcare queries. In Web Conference. 2627–2638

    2024

  29. [37]

    Yiqiao Jin, Yeon-Chang Lee, Kartik Sharma, Meng Ye, Karan Sikka, Ajay Divakaran, and Srijan Kumar. 2023. Predicting Information Pathways Across Online Communities. In KDD

    2023

  30. [38]

    Yiqiao Jin, Andrew Zhao, Yeon-Chang Lee, Meng Ye, Ajay Divakaran, and Srijan Kumar. 2024. Empowering interdisciplinary insights with dynamic graph embedding trajectories. arXiv:2406.17963 (2024)

    2024 arXiv

  31. [39]

    Jeffrey Kuiken, Anne Schuth, Martijn Spitters, and Maarten Marx. 2017. Effective headlines of newspaper articles in a digital environment. Digital Journalism 5, 10 (2017), 1300–1314

    2017

  32. [40]

    Lakkaraju, Jim Mcauley, and Jure Leskovec

    H. Lakkaraju, Jim Mcauley, and Jure Leskovec. 2013. What’s in a name? Understanding the interplay between titles, content, and communities in social media. Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013 (01 2013), 311–320

    2013

  33. [41]

    Yichuan Li, Kyumin Lee, Nima Kordzadeh, and Ruocheng Guo. 2023. What Boosts Fake News Dissemination on Social Media? A Causal Inference View. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 234–246. Manuscript submitted to ACM The Influence of Tex...

    2023

  34. [42]

    Yinhan Liu. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 364 (2019)

    2019 arXiv

  35. [43]

    Steven Loria. 2018. textblob Documentation. Release 0.15 2 (2018)

    2018

  36. [44]

    Jonathas Magalhães, Rubens Pessoa, Cleyton Souza, Evandro Costa, and Joseana Fechine. 2014. A Recommender System for Predicting User Engagement in Twitter. In Proceedings of the Recommender Systems Challenge (RecSys’14) . ACM, Foster City, California, USA, 52–57

    2014

  37. [45]

    Masoud Mazloom, Bouke Hendriks, and Marcel Worring. 2017. Multimodal Context-Aware Recommender for Post Popularity Prediction in Social Media. In Proceedings of the on Thematic Workshops of ACM Multimedia 2017 (Mountain View, California, USA)(Thematic Workshops ’17). Associati...

    2017

  38. [46]

    Philip M McCarthy. 2005. An assessment of the range and usefulness of lexical diversity measures and the potential of the measure of textual, lexical diversity (MTLD). Ph. D. Dissertation. The University of Memphis

    2005

  39. [47]

    AN Medvedev, R Lambiotte, and J-C Delvenne. 2019. The anatomy of Reddit: an overview of academic research. DOOCN 2017, 183–204

    2019

  40. [48]

    Saif M Mohammad and Peter D Turney. 2013. Crowdsourcing a word–emotion association lexicon. Computational intelligence 29, 3 (2013), 436–465

    2013

  41. [49]

    Viet Cuong Nguyen, Mini Jain, Abhijat Chauhan, Heather Jaime Soled, Santiago Alvarez Lesmes, Zihang Li, Michael L Birnbaum, Sunny X Tang, Srijan Kumar, and Munmun De Choudhury. 2024. Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis) Information...

    2024 arXiv

  42. [50]

    OpenAI. 2023. GPT-4 Technical Report. https://openai.com/research/gpt-4

    2023

  43. [51]

    Claudia Orellana-Rodriguez, Derek Greene, and Mark T Keane. 2016. Spreading the news: how can journalists gain more engagement for their tweets?. In Proceedings of the Conference on Web Science (WebSci’16) . ACM, Hannover, Germany, 107–116

    2016

  44. [52]

    Kunwoo Park, Haewoon Kwak, Jisun An, and Sanjay Chawla. 2021. How-to present news on social media: A causal analysis of editing news headlines for boosting user engagement. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 15. 491–502

    2021

  45. [53]

    Alicja Piotrkowicz, Vania Dimitrova, Jahna Otterbacher, and Katja Markert. 2017. Headlines matter: Using headlines to predict the popularity of news articles on twitter and facebook. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 11. 656–659

    2017

  46. [54]

    Alicja Piotrkowicz, Vania Dimitrova, Jahna Otterbacher, and Katja Markert. 2017. The impact of news values and linguistic style on the popularity of headlines on Twitter and Facebook. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 11. 767–774

    2017

  47. [55]

    Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, and Dhanya Sridhar. 2020. Causal effects of linguistic properties.arXiv preprint arXiv:2010.12919 (2020)

    2020 arXiv

  48. [56]

    Reid Pryzant, Youngjoo Chung, and Dan Jurafsky. 2017. Predicting Sales from the Language of Product Descriptions. eCOM@ SIGIR 2311 (2017)

    2017

  49. [57]

    Hemant Purohit, Yiye Ruan, Amruta Joshi, Srinivasan Parthasarathy, and Amit Sheth. 2011. Understanding user-community engagement by multi-faceted features: A case study on Twitter. In Proceedings of the International Workshop on Social Media Engagement . Hyderabad, India

    2011

  50. [58]

    Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics. https://arxiv.org/abs/1908.10084

    2019 arXiv

  51. [59]

    Matthew J Salganik, Peter Sheridan Dodds, and Duncan J Watts. 2006. Experimental study of inequality and unpredictability in an artificial cultural market. science 311, 5762 (2006), 854–856

    2006

  52. [60]

    V Sanh. 2019. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 (2019)

    2019 arXiv

  53. [61]

    Estela Saquete, Jose Zubcoff, Yoan Gutiérrez, Patricio Martínez-Barco, and Javi Fernández. 2022. Why are some social-media contents more popular than others? Opinion and association rules mining applied to virality patterns discovery. Expert Systems with Applications 197 (2022...

    2022

  54. [62]

    Mirela Silva, Fabrício Ceschin, Prakash Shrestha, Christopher Brant, Juliana Fernandes, Catia S Silva, André Grégio, Daniela Oliveira, and Luiz Giovanini. 2020. Predicting misinformation and engagement in covid-19 twitter discourse in the first months of the outbreak. arXiv pr...

    2020 arXiv

  55. [63]

    Matías Gastón Silva, Martín Ariel Domínguez, and Pablo Gabriel Celayes. 2018. Analyzing the retweeting behavior of influencers to predict popular tweets, with and without considering their content. In Annual International Symposium on Information Management and Big Data . Spri...

    2018

  56. [64]

    Edgar A Smith and RJ Senter. 1967. Automated readability index. Vol. 66. Aerospace Medical Research Laboratories, Aerospace Medical Division, Air

    1967

  57. [65]

    Dinuka A Soysa, Denis Guangyin Chen, Oscar C Au, and Amine Bermak. 2013. Predicting YouTube content popularity via Facebook data: A network spread model for optimizing multimedia delivery. In 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) . IEEE, 214–221

    2013

  58. [66]

    Greg Stoddard. 2015. Popularity dynamics and intrinsic quality in reddit and hacker news. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 9. 416–425. https://ojs.aaai.org/index.php/ICWSM/article/view/14636/14485

    2015

  59. [67]

    B. Suh, L. Hong, P. Pirolli, and E. H. Chi. 2010. Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network. In 2010 IEEE Second International Conference on Social Computing . 177–184

    2010

  60. [68]

    Chenhao Tan, Lillian Lee, and Bo Pang. 2014. The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Associat...

    2014 doi

  61. [69]

    Cagri Toraman, Furkan Şahinuç, Eyup Halit Yilmaz, and Ibrahim Batuhan Akkaya. 2022. Understanding social engagements: A comparative analysis of user and text features in Twitter. Social network analysis and mining 12, 1 (2022), 1–16

    2022

  62. [70]

    David Vallet, Shlomo Berkovsky, Sebastien Ardon, Anirban Mahanti, and Mohamed Ali Kafaar. 2015. Characterizing and predicting viral-and-popular video content. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management . 1591–1600. Manusc...

    2015

  63. [71]

    Gaurav Verma, Rynaa Grover, Jiawei Zhou, Binny Mathew, Jordan Kraemer, Munmun Choudhury, and Srijan Kumar. 2024. A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech. In ACL. 12672–12684

    2024

  64. [72]

    Maksims Volkovs, Zhaoyue Cheng, Mathieu Ravaut, Hojin Yang, Kevin Shen, Jin Peng Zhou, Anson Wong, Saba Zuberi, Ivan Zhang, Nick Frosst, et al. 2020. Predicting twitter engagement with deep language models. In Proceedings of the Recommender Systems Challenge (RecSys’20) . ACM,...

    2020

  65. [73]

    Hamilton, and Jure Leskovec

    Alex Wang, William L. Hamilton, and Jure Leskovec. 2016. Learning Linguistic Descriptors of User Roles in Online Communities. In Proceedings of the First Workshop on NLP and Computational Social Science . Association for Computational Linguistics, Austin, Texas, 76–85. https:/...

    2016

  66. [74]

    Huberman

    Chunyan Wang, Mao Ye, and Bernardo A. Huberman. 2012. From User Comments to On-Line Conversations. InProceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Beijing, China) (KDD ’12). Association for Computing Machinery, New York, NY...

    2012

  67. [75]

    Evan Weissburg, Arya Kumar, and Paramveer S Dhillon. 2022. Judging a book by its cover: Predicting the marginal impact of title on Reddit post popularity. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 16. 1098–1108

    2022

  68. [76]

    Honglin Yu, Lexing Xie, and Scott Sanner. 2014. Twitter-driven youtube views: Beyond individual influencers. In Proceedings of the 22nd ACM international conference on Multimedia . 869–872

    2014

  69. [77]

    Victoria Zayats and Mari Ostendorf. 2018. Conversation Modeling on Reddit Using a Graph-Structured LSTM. Transactions of the Association for Computational Linguistics 6 (2018), 121–132. https://doi.org/10.1162/tacl_a_00009 Manuscript submitted to ACM

    2018 doi

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.