{"id":"6f33114b-9ae0-4aea-8e0b-79f5eea26db8","arxiv_id":"2502.00060","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":2,"one_line_summary":"Online discourse on the Israel-Hamas war is highly polarized and dominated by anger and fear across Telegram, Reddit, and Twitter, though evidence for manipulation is only suggestive.","lead":"This study applies topic and sentiment analysis to 125,000 Telegram messages, 2,001 tweets, and 2 million Reddit comments about the Israel-Hamas war. It reports polarized, anger-heavy online narratives and suggests that sentiment patterns may reflect propaganda, but the datasets contain internal inconsistencies.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Dataset description inconsistency: Telegram corpus date range and message counts are irreconcilable, undermining all downstream claims.","rationale":"The reader's REJECT verdict is well-founded, and I agree that the manipulation/propaganda claim is unsupported by the methods. However, the reader's weakest_assumption focuses on temporal representativeness and external dataset provenance; my strongest concern is narrower and more mechanical: the internal dataset description is self-contradictory in ways that prevent a reader from knowing what was actually analyzed. This is load-bearing because the paper's headline contribution is 'a unique dataset of over 125K Telegram messages' and the entire analysis pipeline runs on that dataset. If the numbers do not reconcile, the abstract's findings—polarized narratives, manipulation, propaganda—cannot be tied to a defined corpus. The inconsistency is not merely stylistic; it is a reproducibility failure that blocks evaluation of the central claim. I do not accept the manipulation claim even if the dataset were consistent, because the paper never defines 'outsiders' or 'mold public opinion' operationally, and the sentiment-topic correlation is presented as suggestive ('may show') rather than tested. But the dataset inconsistency is the most concrete, checkable flaw. My recommended verdict remains the same as the reader's REJECT, because the inconsistency compounds the methodological gap: even a charitable reading cannot salvage a timeline that starts in the future. The concrete test—checking the deposited Zenodo archive—is the cheapest way to settle which number is real and whether the abstract's temporal claim is a typo or a fundamental dataset error.","tokens_in":9707,"tokens_out":3105,"duration_ms":27332,"concrete_test":"Reconstruct the exact Telegram dataset from the deposited Zenodo record (doi:10.5281/zenodo.14710657): count messages, compute min/max timestamps, and compare against Table 1 (125,054), the Section 5 figure (70,313), and the Section 5.3.2 analyzed count (51,403). If the deposited data yields none of these numbers or does not match the stated date spans, the corpus description is irreproducible and the abstract's date range ('23 October 2025') is unverifiable, requiring correction or retraction of all dataset-dependent claims.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim—that sentiment-topic correlations reveal manipulation and propaganda by political factions and outsiders—rests entirely on the integrity of the Telegram corpus. That corpus is described inconsistently: the abstract says '125K messages ... spanning from 23 October 2025 until today' (a future date relative to the paper's submission), while Section 5 states the collection spans nine years, with the oldest message on 2015-10-23 and the youngest on 2024-10-24, totaling only 70,313 messages. Table 1 lists 125,054 messages as of 2025-01-20, but Section 5.3.2 says only 51,403 of 70,321 messages were analyzed after deduplication. These are not minor typographical slips: the temporal coverage, the actual sample size, and even the direction of time in the corpus cannot be determined. If the true corpus is 70,313 messages through October 2024, then the abstract's '125K' and its date range are wrong; if the true corpus is 125,054 messages, then Sections 5 and 5.3.2 are wrong. Either way, the volume analysis (Figs 1a–1b), the topic percentages (Section 5.3.1), and the sentiment-topic correlations are computed on an ill-specified dataset. Because the abstract's manipulation claim depends on trends over time ('trends that may show manipulation'), the unanchored timeline destroys the evidential link between observed sentiment patterns and the causal claim about molding public opinion.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper analyzes online discourse about the 2023-2025 Israel-Hamas war using a Telegram corpus, a 2,001-tweet Twitter dataset, and a Reddit dataset of about 2.1 million comments. The stated methods are volume analysis, entity extraction, LDA and BERTopic topic modeling, and sentiment/emotion analysis. The authors report that message volume increased sharply after October 7, 2023, that topics cluster around military operations and humanitarian impact, and that anger and fear dominate the emotional tone. The conclusion goes further, claiming that polarized narratives indicate manipulation and propaganda by political factions and outsiders. The manuscript also deposits a dataset on Zenodo. However, the paper contains severe internal inconsistencies in the reported dataset size and date range, and the causal claim about manipulation is not supported by the descriptive analysis.","tokens_in":9986,"tokens_out":4670,"duration_ms":45891,"significance":"If the descriptive findings were reliable and the dataset were properly documented, the paper could offer a useful case study of multilingual, multi-platform discourse during a geopolitical crisis. The authors have made an effort to share data (Zenodo DOI 10.5281/zenodo.14710657), and the combination of LDA, BERTopic, and emotion analysis is a reasonable toolkit for exploratory work. The main obstacle is that the descriptive results rest on an ill-specified corpus: the paper reports mutually incompatible message counts (125K, 70,313, 70,321, 105K) and an impossible collection start date (23 October 2025). Moreover, the central interpretive claim—that observed sentiment-topic correlations reveal attempts at manipulation and propaganda—has no causal or baseline support and is partly predetermined by choosing already-affiliated Telegram channels. The paper therefore cannot, in its current form, support its advertised conclusions.","major_comments":[{"comment":"The Telegram corpus is described in irreconcilable ways. The Abstract states '125K messages ... spanning from 23 October 2025 until today' (a future date relative to the paper's submission); Section 5 states a 9-year span with the oldest message on 2015-10-23 and the youngest on 2024-10-24, totaling 70,313 messages; Table 1 lists 125,054 messages as of 2025-01-20; Section 5.3.2 says 51,403 of 70,321 messages were analyzed; and Section 7 refers to 105,000 messages. These numbers cannot all be correct, and the abstract's date range is temporally impossible. Because the volume analysis (Fig 1), topic percentages (Section 5.3.1), and sentiment-topic associations are all computed on this corpus, the reader cannot determine the actual sample size or time period, which undermines every downstream quantitative claim and the reproducibility of the study.","section":"Abstract; Section 5; Table 1; Section 5.3.2; Section 7"},{"comment":"The claim that polarized narratives are a 'hallmark of how political factions and outsiders mold public opinion' and that sentiment-topic trends 'may show manipulation and attempts of propaganda' is not supported by the analysis. The study reports descriptive statistics: message frequencies, entity counts, topic proportions, and sentiment/emotion bar charts. There is no measurement of coordinated or inauthentic behavior, no comparison baseline for organic discourse, no identification of actor intent, and no statistical test linking topic-sentiment associations to manipulation. The observed dominance of anger and fear and the presence of military and humanitarian topics are equally consistent with organic audience responses to a violent conflict. Moreover, since the Telegram channels were hand-picked from already-affiliated sources (Section 1.1, Table 1), the 'polarization' conclusion is partly an artifact of sample selection rather than an empirical discovery.","section":"Abstract; Section 7"},{"comment":"The paper claims to apply the same topic and sentiment analysis to Telegram, Twitter, and Reddit, but no topic modeling results are presented for the Reddit dataset. Section 5.3 reports LDA and BERTopic results for Telegram and BERTopic for Twitter; Section 6.3 provides only a single Reddit sentiment bar plot. The cross-platform comparative analysis advertised in the Abstract and in the Contributions list is therefore not actually carried out for Reddit, which is a major gap relative to the paper's stated scope.","section":"Abstract; Section 3; Section 5.3; Section 6.3"},{"comment":"The entity extraction step is not reproducible. Section 5.2 states that hashtags and words were categorized into 'predefined entities' 'based on frequency and manually defined rule,' but no rule, category definitions, inter-annotator agreement, or validation procedure is described. Table 3 mixes hashtags (e.g., '#FreePalestine') with unigrams (e.g., 'gaza') and assigns frequencies without explaining how these are grouped into entities. This makes the subsequent topic interpretation and any claims based on entity prevalence difficult to verify.","section":"Section 5.2"}],"minor_comments":[{"comment":"The manuscript contains numerous typos and mechanical errors, e.g., 'Thesw datasets' (Contributions), 'T able 1' (Table 1 caption), 'weo' in Section 5.3.3, 'enttiets' (Section 5.2), 'discusssed' (Section 5.3.1), and inconsistent capitalization of 'BERTopic'/'BertTopic'. These should be corrected in a thorough revision.","section":"Throughout"},{"comment":"Several references appear as raw LaTeX or are broken, including 'citegayo2011limits,' 'citepreoctiuc2015studying,' 'citeSocialCapitalMarkets' in Section 3, and 'In [?]articleal2019multi' in Section 2. The reference list also has inconsistent formatting; all citations must be properly resolved.","section":"References"},{"comment":"The LDA section states there are 8 topics but later refers to 'topics 6-10' and 'Topic 9' and 'Topic 10' (Section 5.3.1, paragraphs after Fig 9). The number of topics and the topic numbering need to be made consistent.","section":"Section 5.3.1"},{"comment":"The text says the main message volume 'was initiated at the end of 2020' and attributes this to events including the COVID-19 pandemic, while the Conclusion states the volume increased dramatically after October 7, 2023. These statements should be reconciled with the actual time series and the stated collection period.","section":"Section 5.1"},{"comment":"The captions in Appendix 8.1 are generic ('Overall Caption for All Topics') and do not describe the subfigures individually; the subfigures in Fig 20 are labeled with placeholder captions such as 'Caption for Topic 7.' These need to be replaced with informative captions.","section":"Appendix"}],"recommendation":"reject","confidential_remarks":"The manuscript appears to be a very early draft rather than a finished submission: it contains unresolvable numerical inconsistencies, raw LaTeX citation placeholders, and missing analyses. The dataset deposit on Zenodo is a positive contribution, but the current text does not meet the standard for publication, and the central interpretive claim about manipulation would require substantial additional evidence rather than textual revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"What you should know: this paper is a descriptive social-media analysis with one genuinely new asset and several serious problems. The new asset is the claimed Telegram corpus of supposedly 125K messages from conflict-related channels, shared on Zenodo with a DOI. The authors also apply standard topic and sentiment methods to that corpus plus a 2,001-tweet Twitter set and a 2M-comment Reddit dump. That is a reasonable setup for a snapshot study, and the descriptive findings—anger and fear dominate, topics cluster around military and humanitarian themes—are plausible.\n\nThe soft spots, though, are not minor. The dataset description is irreconcilable. The abstract says “125K messages ... spanning from 23 October 2025 until today,” which is a future date relative to the January 2025 submission. Section 5 says the collection spans nine years, oldest message 2015-10-23, youngest 2024-10-24, with only 70,313 messages. Table 1 lists 125,054 messages as of 2025-01-20. The conclusion speaks of 105,000 messages. Section 5.3.2 says only 51,403 of 70,321 messages were analyzed after deduplication. These are not typos; they change the temporal coverage, the sample size, and the direction of time. The volume analysis and any trend-based argument rest on an unanchored timeline.\n\nThat matters because the abstract's headline claim is that sentiment-topic trends “may show manipulation and attempts of propaganda.” Manipulation is a causal claim about intent. The methods here show correlations in a hand-picked set of channels, many already affiliated with one side or the other. The paper explicitly acknowledges the channel selection is not representative. So the supported finding is that polarized, angry discourse exists; the manipulation/propaganda conclusion is not earned.\n\nThe Twitter dataset is a tiny third-party collection of 2,001 tweets, and the Reddit data is a Kaggle upload. The related work already includes prior sentiment-topic analysis of the same conflict on Telegram and Reddit, so the novelty is essentially the Telegram corpus. The manuscript also has an empty “Background” subsection, a malformed citation placeholder, and uneven writing.\n\nCredit where due: the authors do share the corpus, they use standard methods appropriately for a descriptive study, and they acknowledge demographic limitations of social-media data. But the internal contradictions make the dataset unusable in its current form, and the causal language overreaches.\n\nWho is this for? A reader wanting a quick descriptive overview of conflict discourse across platforms might get something, but only if they ignore the data-evaluation problems. I would not cite this in my own work, and I would not send it to a serious referee until the dataset description is corrected and the manipulation claims are removed or substantially reworked. The right move is to ask the authors for a corrected version, then reconsider.","headline":"The paper's own data description is internally contradictory (125K vs 70K vs 105K messages; 2025 vs 2015-2024), so the central claim about manipulation is not supported and the dataset cannot be used as-is.","tokens_in":10505,"tokens_out":1602,"would_cite":false,"duration_ms":17984,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that online discussion of the Gaza war is polarized into military and humanitarian narratives, with anger and fear dominant.","keywords":["Israel-Palestine conflict","Telegram","social media discourse","BERTopic","sentiment analysis","topic modeling","polarization","propaganda"],"falsifier":"Rebuild the corpus with random or exhaustive collection and verified timestamps, ideally all Telegram messages in these channels plus a time-matched Twitter sample from the same months, and rerun the identical topic and emotion pipeline; if anger and fear dominance and the military-versus-humanitarian split shrink or vanish, the polarization result is an artifact of which channels and posts were selected.","tokens_in":9500,"feed_emoji":"📊","tokens_out":4353,"duration_ms":39587,"temperature":0.7,"pith_summary":"This paper tries to establish that online discussion of the Israel–Hamas war, especially on Telegram, is dominated by two opposed narrative poles—military operations and civilian suffering—and by the emotions of anger and fear. The authors argue that this polarized emotional structure is the signature of how political factions and outside actors shape public opinion during the conflict, and they treat sentiment-topic patterns as possible evidence of manipulation and propaganda. They support the claim with volume analysis, entity extraction, LDA and BERTopic topic modeling, and sentiment and emotion classification across 125,054 Telegram messages, 2,001 tweets, and about 2.1 million Reddit comments. A sympathetic reader would care because the finding would turn social-media emotion into a measurable indicator of coordinated narrative-shaping in real-time crises.","feed_headline":"Anger and fear dominate war talk across platforms","feed_subtitle":"Analysis of 125K Telegram messages plus public Twitter and Reddit posts ties polarized talk to propaganda attempts.","key_machinery":"The load-bearing machinery is a pipeline of four techniques applied to text corpora: volume analysis over time and channels; entity extraction from frequent hashtags and words; topic modeling with LDA and BERTopic, a transformer-embedding method that clusters semantically similar messages into topics; and sentiment and emotion classification using pretrained Hugging Face models, including a fine-grained emotion model that labels anger, fear, hope, and similar states. LDA and BERTopic supply the topic clusters; the sentiment and emotion classifiers supply the emotional tone; and the paper treats the correlation between the two as evidence about how narratives are being shaped.","core_discovery":"On the paper's own terms, the central discovery is that conflict discourse across Telegram, Twitter, and Reddit is emotionally polarized and topically clustered: anger and fear dominate, especially in channels reporting combat directly, and the discussion splits into humanitarian themes (Gaza, children, hospitals, displacement) and military or operational themes (strikes, brigades, occupation, resistance). The authors interpret this configuration as polarized narratives being the hallmark of how political factions and outsiders mold public opinion, with the sentiment-topic alignment taken to reveal trends that may show manipulation and attempts of propaganda. In short, the paper claims that the emotional tone of what people say online during the war is not a random reflection of events but a structured product of narrative competition.","pith_inferences":["The paper's own cumulative volume plots show message numbers rising sharply after late 2020 and especially after 7 October 2023; a natural extension would compare the emotion mix before and after that spike to separate event-driven anger from sustained narrative-driven anger.","With only 2,001 tweets, the Twitter leg is too thin for platform-level conclusions; the same pipeline on a larger, time-matched Twitter sample would test whether the Telegram polarization pattern generalizes.","If outside actors truly mold opinion, their fingerprint should be coordinated hashtag bursts across otherwise unconnected channels; the entity table offers candidate tags such as #FreePalestine and #GazaUnderAttack for a burst-coordination test the paper does not run.","The paper states the Telegram collection period inconsistently, as starting on 23 October 2025 in the abstract and as spanning 2015 to 2024 in the methodology; resolving that dating would determine whether the reported volume and emotion patterns describe one continuous corpus or two different collections."],"forward_implications":["If the reading is right, Telegram is not just a messaging app in this conflict but a primary arena where military and humanitarian narratives compete for emotional engagement.","Topic clusters of about 19 percent general Palestinian issues and 17.9 percent geographical and humanitarian aspects imply that a large share of discourse centers on civilian impact, not strategy.","The dominance of anger and fear across conflict-reporting channels implies that emotion, not information, carries the engagement in these spaces.","Sentiment-topic alignment, if confirmed, gives platforms and researchers a measurable signal for detecting possible propaganda campaigns during crises."],"supporting_citations":[{"why":"Supplies the Twitter dataset of 2,001 tweets split into gaza, israel, palestine, hamas, and combined files, used for the cross-platform comparison.","marker":"[12]"},{"why":"Supplies the Reddit dataset of about 2.1 million comments on the Israel-Palestine war, used for volume and sentiment analysis.","marker":"[13]"},{"why":"Provides baseline sentiment-analysis accuracy and stance distribution on Reddit conflict comments, which this paper's Reddit sentiment analysis extends.","marker":"[11]"},{"why":"Quantifies extreme opinions on Reddit during the 2023 Israeli-Palestinian conflict, serving as the closest prior finding for the polarization claim.","marker":"[7]"},{"why":"Documents an Israeli Telegram channel used to incite violence against Palestinians, grounding the claim that Telegram channels advance polarized narratives.","marker":"[3]"},{"why":"Establishes how narrative construction and sentiment manipulation operate on social media, underpinning the interpretation of sentiment-topic correlations as propaganda.","marker":"[2]"}],"fun_headline_variants":["Anger, fear, and propaganda shape war sentiment online","Polarized narratives dominate Israel-Hamas social media talk","Study: Fear and anger steer online war discourse on Gaza","Social media war talk reveals polarized emotional camps"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole argument rests on the assumption that the hand-picked Telegram channels, the 2,001-tweet set, and the Kaggle Reddit dump collectively stand in for conflict discourse, and that the observed emotion-topic patterns reflect narrative manipulation rather than ordinary, organic reactions to a war.","fun_headline_variants_meta":{"raw":{"variants":["Anger, fear, and propaganda shape war sentiment online","Polarized narratives dominate Israel-Hamas social media talk","Study: Fear and anger steer online war discourse on Gaza","Social media war talk reveals polarized emotional camps"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00052,"raw_usage":{"total_tokens":2548,"prompt_tokens":1005,"completion_tokens":1543,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":621,"completion_tokens_details":{"reasoning_tokens":1479}},"tokens_in":621,"tokens_out":1543,"duration_ms":11974,"temperature":1.0,"reasoning_tokens":1479,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T00:19:48.537498+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Rebuild the corpus with random or exhaustive collection and verified timestamps, ideally all Telegram messages in these channels plus a time-matched Twitter sample from the same months, and rerun the identical topic and emotion pipeline; if anger and fear dominance and the military-versus-humanitarian split shrink or vanish, the polarization result is an artifact of which channels and posts were selected.","supporting_citations":[{"cited_title":"GitHub-rizqikapratamaa; 2024","cited_arxiv_id":null,"evidence_quote":"Supplies the Twitter dataset of 2,001 tweets split into gaza, israel, palestine, hamas, and combined files, used for the cross-platform comparison."},{"cited_title":"Daily Public Opinion on Israel-Palestine War; 2024","cited_arxiv_id":null,"evidence_quote":"Supplies the Reddit dataset of about 2.1 million comments on the Israel-Palestine war, used for volume and sentiment analysis."},{"cited_title":"Sentiment Analysis of Israel-Palestine Conflict Comments Using Sentiment Intensity Analyzer and TextBlob","cited_arxiv_id":null,"evidence_quote":"Provides baseline sentiment-analysis accuracy and stance distribution on Reddit conflict comments, which this paper's Reddit sentiment analysis extends."},{"cited_title":"Quantifying Extreme Opinions on Reddit Amidst the 2023 Israeli-Palestinian Conflict","cited_arxiv_id":null,"evidence_quote":"Quantifies extreme opinions on Reddit during the 2023 Israeli-Palestinian conflict, serving as the closest prior finding for the polarization claim."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Documents an Israeli Telegram channel used to incite violence against Palestinians, grounding the claim that Telegram channels advance polarized narratives."},{"cited_title":"Narratives online: Shared stories in social media","cited_arxiv_id":null,"evidence_quote":"Establishes how narrative construction and sentiment manipulation operate on social media, underpinning the interpretation of sentiment-topic correlations as propaganda."}],"review_version":1}