Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

Abeer Aldayel; Walid Magdy

REVIEW 3 major objections 5 minor 57 references

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A user's stance toward a topic can be read from their online network footprint alone, and combining network signals with tweet text reaches 72.49% F1, the highest reported on the SemEval stance dataset.

desk verdict Network-based stance detection with a new likes timeline feature is a real contribution, but the headline F1 claim rests on an unreported split-leakage check and an apples-to-oranges comparison. read the letter →

arxiv 1908.03146 v1 pith:YKPENDA2 submitted 2019-08-08 cs.SI cs.CL

Abeer Aldayel , Walid Magdy This is my paper · ORCID

classification cs.SIcs.CL

keywords stancedetectionsocialmediaTwitternetworkfeatureshomophilyuserprivacysilentusersonlinefootprint

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 asks whether a social media user's stance on a topic, in favor or against, can be read from signals other than what they post. On the five-topic SemEval Twitter benchmark, it shows that network-based features alone, such as whom the user interacts with, follows, likes, and which web domains they link, detect stance about as well as text-only models and often better. It then reports that combining these network signals with tweet text reaches an F-measure of 72.49% on the benchmark, the highest figure reported for that dataset at the time. The broader claim is that stance is embedded in a user's online footprint, so even users who never mention a topic can be profiled.

What carries the argument

The central mechanism is homophily, the tendency of users with shared beliefs to touch overlapping accounts and domains, turned into three user-level feature sets: the interaction network, the accounts or domains a user retweets, replies to, mentions, or links; the preference network, the accounts or domains appearing in tweets the user liked; and the connection network, the accounts the user follows or is followed by. Each user is represented as a Boolean presence vector over these sets and fed to a linear SVM, whose weights also make the influential accounts and domains interpretable. Because the three networks overlap by only about 10 to 20 percent, the argument is that each supplies an independent proxy for the same underlying stance signal.

What would settle it

Check whether any user ID appears in both the SemEval training and test sets; if so, rebuild the splits so that no user spans both and re-run the SVM experiments to see whether the 72.49% F1 drops toward the text-only baseline.

Watch

Extended reading notes

Core claim

The discovery, on the paper's own terms, is that on the SemEval 2016 stance dataset, user-level network features outperform the benchmark's strongest text-based system and that the two kinds of signal are additive. Using a linear SVM with Boolean presence vectors over sets of accounts and domains, the interaction network reaches an F1 of 71.56% in the two-class favor/against setting, above the 69.80% text baseline, and adding text to those network features yields 72.49%. The preference network built from liked tweets and the connection network built from followees also beat the text baseline, implying that a user's likes and follow relationships are sufficient evidence of stance even when the user publishes no topical content.

Load-bearing premise

The central load-bearing premise is that the retrieved subset of users and the train/test split are representative and user-disjoint, since 167 users authored multiple tweets and identical network features for a repeated user would give the classifier an identity shortcut that could inflate the reported F1.

Editorial extensions

If this is right

Stance can be inferred for silent users who never post about a topic, as long as their likes or follow relationships overlap with other users' networks.
Content-free network features are a viable substitute for text models when topical text is scarce, unavailable, or deliberately absent.
Combining text with the interaction-network features gives the highest reported F1 on the SemEval stance benchmark, indicating that content and network carry complementary signals.
Forcing a binary favor/against decision outperforms allowing a neutral class, even though it misclassifies neutral tweets, because the gain in recall on polarized stances outweighs the added false positives.
Public social-media footprints, including likes and follows, are enough to profile users' political, religious, and social leanings, which has direct privacy implications.

Reading between the lines

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

If the near-equivalence of three largely disjoint networks generalizes, then stance signal is distributed across every kind of social contact, and a user cannot hide their stance by avoiding one interaction type.
A natural stress test is to re-split the benchmark so no user appears in both training and testing; if the 72.49% figure drops toward the text-only baseline, part of the reported gain would come from user identity rather than from network signal.
The same feature sets could be ported to other platforms and languages; if the result holds there, stance profiling of non-posting users becomes a general privacy concern rather than a Twitter-specific one.
An adversarial implication is that users could try to camouflage their footprint by following or liking balanced sets of accounts, turning stance leakage into an arms race between profilers and users.

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Signed reviews

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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

Colleague, here's my read on Aldayel & Magdy's stance detection paper. The real contribution is the preference network: using the accounts and domains in a user's likes timeline as a stance signal, and comparing it head-to-head with interaction network, connection network, and text on five SemEval topics. That comparison, plus the feature-level analysis showing which accounts and domains separate favor from against per topic, is genuinely useful and not in the prior literature. The qualitative examples in Section 6.3 are a strength: they show the mechanism—users reveal stance by following, liking, and retweeting topically unrelated accounts. The privacy angle is well argued.

The softer spots are all around the headline number. The 72.49% is computed on a reduced test subset (906 of 1249 tweets) because 25% of accounts were deleted or suspended. The paper compares that number against published F1 on the full SemEval test set. That is apples to oranges. Their own text baseline on the reduced set is 69.80 vs 68.98 on the full set, so the reduced set is not neutral to the comparison. The stress-test concern about user leakage is also legitimate. Since network features are user-level and 167 users have multiple tweets, if any user appears in both train and test, the SVM sees exact feature copies and can memorize identity. The paper never states that the split is user-disjoint. It might well be—SemEval likely was designed that way—but it needs to be verified and reported. If there is overlap, the SOTA claim collapses and the 'unseen users' claim weakens. The significance test is also a bit shaky: the t-test is significant but the Mann-Whitney U test is not, and they still lean on the t-test.

None of this kills the paper. The central direction—network-only features comparable to text, and the new PN feature—is plausible and the analysis is careful. The quantitative claims just need to be re-baselined on the same split, and the leakage question answered explicitly.

For peer review: I'd send it out. It's a worthwhile empirical study that will be of interest to the CSCW and social media analysis community. The revisions are clear: verify user-disjoint split, report baselines on the same test subset, and either remove or caveat the SOTA claim. If those are addressed, it's a solid contribution. If not, it's still a useful qualitative study but the headline should be withdrawn.

Referee Report

3 major / 5 minor

Summary. The paper studies whether Twitter users' stances toward five SemEval-2016 topics can be inferred from user-level network features rather than solely from on-topic tweet text. It defines four feature groups: on-topic content (TXT), interaction network (IN: mentions and domains in home timeline), preference network (PN: mentions and domains in liked tweets), and connection network (CN: friends/followers), and trains linear SVMs in three-class and two-class (favor/against) settings. On the subset of users whose accounts could be retrieved, it reports that network features alone are comparable to or better than a text-only baseline, and that combining TXT with IN achieves an average F-measure of 72.49%, which it calls the highest reported performance to date on the SemEval dataset. It then analyzes feature overlap, most influential accounts and domains per topic, and qualitative examples to argue that stance is strongly embedded in users' online social footprint, with implications for privacy.

Significance. If the results hold, the paper makes a useful contribution to stance detection and privacy research: it broadens the feature space beyond text to include preferences and connections that can apply to silent users, tests generalization across five topics from different domains, and provides an extensive analysis of which network signals matter. The public release of the collected network data and the careful comparison of feature groups are strengths. The main caveat is that the headline comparison is not yet demonstrated on the full SemEval test set or on a split that is shown to be user-disjoint, so the quantitative claims need verification before the 'highest reported' statement can be accepted.

major comments (3)

[§5.1, Table 5(B) and Table 2] The headline claim of 72.49% F-measure is computed on the reduced test subset of 906 tweets (Table 2), whereas the published state-of-the-art values to which it is compared (70.03% in [46], 68.98% in [40]) are reported on the full 1,249-tweet SemEval test set. Because the 343 excluded tweets come from accounts that were deleted or suspended, the subset is not a random sample, and the paper does not show that performance on this subset is representative of performance on the full test set. To support the 'highest reported' claim, the authors should either evaluate their models on the full test set or, at minimum, re-run the published baselines on the same 906-tweet subset and explicitly report the comparison as subset-restricted rather than dataset-level.
[§3.1, Table 2] The paper does not state whether the SemEval train/test split, or the reduced existing-users split in Table 2, is user-disjoint. Section 3.1 reports that 167 users authored multiple tweets, and because IN, PN, and CN features are user-level, every tweet from the same user receives identical feature vectors. If any of these users appears in both train and test, the test set would contain feature vectors that are exact copies of training vectors, allowing the SVM to exploit an identity shortcut rather than learning a generalizable stance signal. Please report the number of users appearing in both splits for both the full and reduced datasets, and if any overlap exists, re-run the evaluation with a user-disjoint split or leave-one-user-out validation.
[§5.1] The claim that the combined model is statistically significantly better than the baseline is supported only by 'two-tailed t-test with p-value < 0.05', with the Mann-Whitney U test not significant. No test statistic, degrees of freedom, or per-topic scores are given, and with only five topic-level F-measures the power of such a test is extremely limited. Please provide the full test details and either report the p-values for both tests or soften the significance claim; as written, the significance statement is not checkable.

minor comments (5)

[Tables 3 and 4] The column headers contain garbled characters such as 'Ff avour' and 'Faдainst'; the Cyrillic 'д' should be 'd', and the headers should be regenerated.
[§3.3] The text says 'cross-validation with k=5' but does not specify what is tuned or whether the test set is used only once; please clarify the model selection procedure.
[Figures 2 and 3] The captions and axis labels appear corrupted with Unicode placeholder characters and cannot be read; please regenerate the figures.
[References] Reference [11] appears to carry the same DOI as reference [10]; please verify and correct the DOI.
[Footnotes 1 and 6] Footnote 1 provides a GitHub URL for the data, while footnote 6 says the list of ids and network information 'would be made available'; please make the availability statement consistent and explicit.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are empirical comparisons against an external benchmark with independently collected features.

full rationale

This is an empirical machine-learning study with no fitted constants, no analytical derivation of the target quantity from itself, and no uniqueness theorem exported from prior author work. The labels come from the external SemEval 2016 stance dataset, and the network features (IN, PN, CN) are collected from the Twitter REST API independently of both the target tweet text and the stance labels. The headline result (F-measure 72.49% in Table 5(B)) is a held-out performance measurement of an SVM trained on text plus interaction-network features, evaluated with the official SemEval macro-F1 script. The comparison baseline TXT is retrained on the same reduced subset of users whose network data could be retrieved, which makes the comparison fair rather than circular. The paper does cite prior work by the authors (e.g., [3], [14], [36]), but these citations are used only to motivate the homophily-based feature design and are not load-bearing evidence for the measured performance; the claim stands on the benchmark evaluation itself. The one substantive concern raised by reviewers, namely whether the SemEval train/test split or the reduced subset keeps users disjoint across splits, is a data-hygiene and leakage question, not a circularity in the derivation: even if leakage existed it would inflate results via an identity shortcut, not make the output equivalent to the input by construction. No step in the paper reduces a predicted quantity to a fitted parameter or to a self-citation chain, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new free parameters fitted to the test data; standard SVM settings and n-gram ranges are inherited from prior work. The load-bearing assumptions are dataset-level: user stability, representativeness of the surviving subset, user-disjoint splits, and homophily. No invented entities.

assumptions (4)

domain assumption User stance on a topic is stable over the collection period.
Section 3.1 assumes a user's stance does not change within a short period, justifying assigning the same network features to all tweets from a user. They provide a small supporting analysis (104/167 users with fixed stance), but it is an assumption.
domain assumption The retrieved subset of users (75% of the original) is representative of the full SemEval population.
Section 4.1 reports that about 25% of users were deleted or suspended and all experiments run on the remaining subset; the paper does not analyze whether this attrition correlates with stance or topic.
domain assumption No user appears in both train and test partitions.
The paper notes 167 users produced multiple tweets but never checks whether the SemEval split is user-disjoint; if users cross the split, user-level features could leak labels.
domain assumption Homophily: users with similar stance follow and interact with similar accounts.
Section 1 and 2.3 rely on homophily to motivate network features; this is a background social-science assumption supported by cited literature, not proven in this paper.

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Pith. "Pith review of Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media." pith.science (2026). https://pith.science/paper/YKPENDA2

@misc{pith2026190803146,
  author       = {Pith},
  title        = {Pith review of: Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YKPENDA2}},
  note         = {Machine review of arXiv:1908.03146}
}

read the original abstract

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%. We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.

Figures

Figures reproduced from arXiv: 1908.03146 by the authors.

**Figure 2.** Similarity between CN, IN and DM in users dataset. [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** Similarity between CN, IN and DM for (In-favor and Against) stances with respect to the top features. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

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