Recipient Profiling: Predicting Characteristics from Messages

Damien Sileo; Martin Borquez; Michael Perrot; Mikaela Keller

REVIEW 4 major objections 5 minor 33 references

Recipient Profiling: Predicting Characteristics from Messages

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The gender of a message's intended recipient can be predicted from text alone.

desk verdict New task formulation worth a look; the SWDA evidence may be an artifact, so the feasibility claim rests on MDC and TIC. read the letter →

arxiv 2412.12954 v1 pith:6W36WA6V submitted 2024-12-17 cs.CL

Martin Borquez , Mikaela Keller , Michael Perrot , Damien Sileo This is my paper · ORCID

classification cs.CL

keywords recipientprofilinggenderpredictionprivacyleakagetextclassificationlanguageaccommodationtransferlearningconversationdatasetsauthor

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

The paper introduces 'recipient profiling,' a counterpart to author profiling: instead of inferring what a text reveals about its writer, it asks what a message reveals about the person it was written to. The authors formalize this as a classification task and test it by predicting the recipient's gender from individual messages using fine-tuned pretrained language models. On three very different corpora—telephone conversations, movie dialogues, and post-match tennis interviews—all models beat chance, and models trained on one corpus still beat chance on the others. The authors argue that if this holds, message content is a genuine privacy channel for recipients, not just authors, and that the task can also expose how speakers accommodate their audience.

What carries the argument

The machine that carries the argument is the recipient-profiling classifier $f: \mathcal{U}_{A\to R} \to S$, a map from messages addressed by authors to recipients into a sensitive-attribute space such as gender. Because individual messages are often too short to be informative, the paper concatenates utterances from the same conversation to a minimum length before classification. The empirical machinery consists of fine-tuning three pretrained text encoders—BERT, MPNet, and DeBERTa—with a single linear classification layer, training and testing on recipient-disjoint splits so that no recipient appears in more than one split, and scoring with balanced accuracy. The transfer experiments then ask whether a classifier trained on one corpus still classifies recipients in another, testing whether the learned signal is domain-general.

What would settle it

Mask all direct identifiers in the test messages—recipient names, gendered honorifics and titles, and third-person references to the recipient—then retrain and evaluate the same models. If balanced accuracy drops to chance on all three datasets, the claimed recipient-specific signal beyond direct identifiers is not supported; if it stays above chance, the accommodation signal is real.

Watch

Extended reading notes

Core claim

The paper claims that messages carry a recoverable signal about the intended recipient, not just the author, and that this signal is strong enough to classify the recipient's gender above chance from text alone. Fine-tuned BERT, MPNet, and DeBERTa models reach balanced accuracies of about 0.77 on Switchboard, 0.60 on the Movie Dialog Corpus, and 0.68 on the Tennis Interviews Corpus, and models trained on one corpus exceed chance (0.51–0.58) on the other two. The authors take this as evidence that recipient profiling is feasible and that it constitutes a new privacy risk inherent in textual messages, one that runs parallel to the better-studied risk of author profiling.

Load-bearing premise

The above-chance accuracy is evidence about recipients only if it comes from how people write to different recipients, not from direct identifiers such as recipient names, 'sir'/'ma'am', or other labels already present in the message; the paper states that it did not check this distinction.

Editorial extensions

If this is right

Recipient gender classification is feasible on at least three distinct conversation genres, so recipient profiling can be treated as a measurable task rather than a hypothetical one.
Because models transfer across datasets at above-chance accuracy, at least part of the signal generalizes beyond a single corpus's vocabulary or topic distribution.
Message collections that include recipient metadata now count as privacy-relevant in the same way author-labeled text does, and recipient-side anonymization becomes a concrete design target.
Recipient-profiling models offer a quantitative probe for communication accommodation theory, turning a documented stylistic phenomenon into a testable predictor.

Reading between the lines

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

The paper leaves untested whether the signal comes from direct identifiers (names, 'sir'/'ma'am') or from indirect stylistic accommodation; masking those direct identifiers and retraining would show which channel drives the result.
The higher accuracy for female recipients is left unexplained; comparing messages whose topics are held fixed across recipient genders would indicate whether the asymmetry is about the recipient or about topic conventions.
Transfer accuracies sit only slightly above chance, so the practical privacy risk may be domain-dependent; a realistic attacker evaluation would need messages from unseen communication channels, not just held-out recipients from the same corpus.

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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

The paper formalizes recipient profiling—predicting a recipient's attributes from messages addressed to them—as a task distinct from author profiling. That framing is genuinely new, and the proof-of-concept idea is reasonable: if writers accommodate to their audience, the recipient's gender should leave traces in the text. The authors also release the datasets, and the same-domain results are consistent across three encoders with small standard deviations. The limitation section is unusually honest, especially the admission that they did not investigate direct versus indirect identifiers.

The soft spots, though, are real and one is potentially load-bearing. The stress-test note about SWDA is correct on the face of it: the preprocessing concatenates consecutive segments from the same two-party conversation until 1,000 characters, which, without a speaker-side filter, mixes both speakers' turns. The classifier then sees the recipient's own words, turning the SWDA experiment into author profiling. This would explain why SWDA accuracy (0.75–0.77) is far above MDC (0.59–0.60) and TIC (0.68). The paper does not report grouping by speaker, so this must be checked in the released code before the central claim is accepted. If the leak is real, the strongest quantitative support disappears.

Even without the SWDA issue, the MDC and TIC numbers are above chance but modest, and the authors acknowledge they did not control for direct identifiers like names or gendered titles. If the signal is mostly those, the privacy-leak interpretation loses much of its force. The transfer results (0.51–0.58) are close to chance, which the authors honestly describe as a loss in accuracy, but it is still weak evidence of transferable accommodation.

The paper is worth serious peer review because the task formulation is new and the evaluation, once the SWDA preprocessing is fixed, would be straightforward to redo. The authors' own limitations section shows they are aware of the main threats to validity, which is a good sign. My recommendation: send it to review, but require the authors to verify whether SWDA chunks are speaker-separated, rerun if not, and add a direct-identifier ablation or at least an explicit analysis of what the models rely on. With those changes, the paper could make a solid contribution to privacy and communication-accommodation research.

Referee Report

4 major / 5 minor

Summary. The paper proposes a new NLP task, Recipient Profiling: predicting a sensitive attribute (here, gender) of the intended recipient of a message, as opposed to the author. The authors formalize the task, then provide proof-of-concept experiments on three conversation datasets (SWDA, MDC, TIC) using three pretrained encoders (BERT, MPNet, DeBERTa) fine-tuned with a linear classification head. They report above-chance balanced accuracy on all same-domain settings, with the largest effects on SWDA (0.75–0.77) and TIC (0.67–0.68), and weaker but above-chance cross-dataset transfer (0.51–0.58). The paper also reports a female-vs-male accuracy gap and a between-model agreement analysis. The authors frame the result as evidence that messages leak information about the recipient, raising new privacy concerns.

Significance. If the results hold, the paper introduces a genuinely new task that is a natural counterpart to author profiling and has plausible privacy implications. The authors provide a clean task formalization, use recipient-based held-out splits, report balanced accuracy with standard deviations over three seeds, and release the preprocessed datasets on HuggingFace, which supports reproducibility. The central claim, however, is stronger than the evidence: the above-chance accuracy could be driven by trivial cues (recipient names, titles) or by a preprocessing artifact in SWDA, and the paper explicitly acknowledges that direct vs. indirect identifiers were not separated. The significance therefore hinges on resolving these confounds; as it stands, the paper is a useful proof-of-concept of the task's feasibility, but the privacy-leak interpretation is not yet established.

major comments (4)

[Section 4 (SWDA preprocessing)] The preprocessing description states that 'we concatenated segments from the same conversations until we reached a limit of 1,000 characters' without specifying that the concatenation is restricted to a single speaker's turns. In the two-party Switchboard conversations, consecutive dialogue acts normally alternate between the two speakers; if each 1,000-character chunk contains both the author's and the recipient's utterances, then the classifier can infer the recipient's gender from the recipient's own words, turning the SWDA experiment into author profiling rather than recipient profiling. This would also explain why SWDA accuracy (0.75–0.77) is far above MDC (0.59–0.60) and TIC (0.67–0.68). Please clarify whether the concatenation grouped by speaker, and ideally add an experiment that removes the recipient's own turns from the input (or reports results on author-only segments) to confirm that the signal resides in the author's messages.
[Sections 5.1, 6, and 7] The conclusion that recipient profiling 'constitutes a new privacy risk' assumes that the predictions are based on indirect linguistic accommodation rather than direct identifiers such as recipient names, pronouns, or titles like 'sir'/'ma'am'. Section 7 explicitly acknowledges that the paper 'did not investigate ... whether their predictions were due to direct or indirect identifiers.' This is a load-bearing gap because direct identifiers would make the task trivial and would not support the stated accommodation-based interpretation. I recommend adding a simple ablation (e.g., masking names and gendered address terms, or comparing against a model trained only on such features) or explicitly rephrasing the privacy claims to reflect that the source of the signal is unknown.
[Section 5.2] The cross-dataset transfer claim rests on balanced accuracies in the narrow interval [0.51, 0.58] being 'better than chance,' but no significance tests are reported and the text does not list the standard deviations for the transfer matrix (they appear only in the figure). With three seeds, a 0.51 mean may not be reliably above 0.5. The authors should report confidence intervals or a per-cell permutation/binomial test against chance to support the transferability claim.
[Section 5 (overall)] Throughout the results, the evidence for 'above-chance' performance is limited to means and standard deviations over three seeds. No statistical significance tests are provided for any comparison to chance or between models. Given that the central claims are 'better-than-chance' outcomes, adding a minimal significance test (e.g., a bootstrap or permutation test on the balanced accuracy) would materially strengthen the conclusions.

minor comments (5)

[Section 7] The sentence 'There is two main aspects to our work' should be 'There are two main aspects'.
[Section 4] The split description says '80% of recipients in the train set and 20% distributed in the test and validation sets,' but Table 1 gives 352/18/70 recipients out of 440, which is 80.0% train, 4.1% validation, and 15.9% test; please clarify the exact proportions.
[Abstract and Section 1] The data link appears as '[data:HF-datasets ]' with an incomplete bracket; ensure the URL and its formatting are correct in the final version.
[Equation (1)] The inline expression for kappa is ambiguous: 'κi,j = Pi,j − Ri,j / 1 − Ri,j' should be properly typeset as (Pi,j − Ri,j)/(1 − Ri,j).
[Figure 3] The caption contains a typo: 'The models were train and test with in the same domain' should be 'trained and tested in the same domain.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the recipient profiling result is an empirical feasibility finding evaluated on held-out recipients and cross-domain transfer, with no equation-level reduction to its inputs.

full rationale

The paper's central claim is an empirical feasibility result, not an analytic derivation. The evaluation uses a recipient-based group split (Section 4: 'We split the datasets into train, validation, and test sets with a recipient-based group split, so that no recipient appears in multiple splits, to prevent overfitting'), held-out test recipients, standard supervised fine-tuning, and cross-dataset transfer experiments. No fitted parameter is renamed as a prediction, and no equation in the paper reduces the reported balanced accuracy to its own input by construction. The only self-citation, Sileo (2023) as the DeBERTa training source, is a model choice rather than a load-bearing premise, and the same conclusion is obtained with BERT and MPNet as well. The Section 7 limitation that the authors 'did not investigate ... whether their predictions were due to direct or indirect identifiers' is an interpretability and validity caveat, not a circularity, because it does not make the measured result equivalent to the training signal. The possible SWDA preprocessing concern raised by the reviewer, in which concatenating consecutive segments from a two-party conversation could mix author and recipient turns, is a data-construction artifact that could threaten the empirical claim, but it is not a derivation that reduces to its own input, so it does not raise the circularity score under the stated rules. The derivation chain is therefore self-contained with respect to circularity.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central feasibility claim relies on three hand-chosen preprocessing choices (concatenation length, class subsampling, fixed hyperparameters) and three domain assumptions about label quality, text sufficiency, and model adequacy. No new entities are posited. These are ordinary experimental assumptions for a proof-of-concept, and the paper itself flags the main confound (direct vs indirect identifiers) in its limitations section.

free parameters (3)

SWDA concatenation length threshold = 1000 characters
Hand-chosen in Section 4; utterances shorter than the limit are concatenated within a conversation, reducing 122,646 utterances to 9,030 and affecting the signal available per example.
Majority-class subsampling = random subsample to class size
Section 4: the overrepresented gender class is randomly subsampled to match the underrepresented class (Kubat and Matwin, 1997), changing dataset composition and variance across the three seeds.
Fine-tuning hyperparameters = learning rate 2e-5, 3 epochs, Adam
Fixed without tuning (Section 4); these choices affect measured accuracy, though the above-chance result is likely robust to small variations.

assumptions (3)

domain assumption Recipients are labeled with an accurate binary gender in all three datasets.
Invoked in Section 4 for dataset selection; if labels are noisy, the measured balanced accuracy underestimates the true signal.
domain assumption The concatenated or raw message text preserves the signal about the recipient.
Section 3 assumes a concatenation of messages can reach a minimal base length without destroying the cue; the SWDA preprocessing makes this explicit.
ad hoc to paper A linear classification head on a pretrained text encoder is an adequate model family for the task.
Section 4 selects BERT, MPNet, and DeBERTa without a motivation beyond their effectiveness in author representation; the feasibility result is contingent on this model class.

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Pith. "Pith review of Recipient Profiling: Predicting Characteristics from Messages." pith.science (2026). https://pith.science/paper/6W36WA6V

@misc{pith2026241212954,
  author       = {Pith},
  title        = {Pith review of: Recipient Profiling: Predicting Characteristics from Messages},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6W36WA6V}},
  note         = {Machine review of arXiv:2412.12954}
}

read the original abstract

It has been shown in the field of Author Profiling that texts may inadvertently reveal sensitive information about their authors, such as gender or age. This raises important privacy concerns that have been extensively addressed in the literature, in particular with the development of methods to hide such information. We argue that, when these texts are in fact messages exchanged between individuals, this is not the end of the story. Indeed, in this case, a second party, the intended recipient, is also involved and should be considered. In this work, we investigate the potential privacy leaks affecting them, that is we propose and address the problem of Recipient Profiling. We provide empirical evidence that such a task is feasible on several publicly accessible datasets (https://huggingface.co/datasets/sileod/recipient_profiling). Furthermore, we show that the learned models can be transferred to other datasets, albeit with a loss in accuracy.

Figures

Figures reproduced from arXiv: 2412.12954 by the authors.

**Figure 1.** Performance of fine-tuned models for recipient gender classification in terms of balanced accuracy. The barplot shows the performance of each model when trained and tested within the same domain. The error bars represent the standard deviation of the measurements, calculated after running each model with three different seeds. 2023). We added to all the models a classification head consisting in a single linear l… view at source ↗

**Figure 2.** Balanced accuracy transfer performance of fine-tuned models for recipient gender classification. The [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Difference in accuracy of fine-tuned models when predicting recipient gender. The values represent the [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Agreement between fine-tuned models for recipient profiling, measured using the Kappa Coefficient to [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Bartholomew Ayeni. 2021. https://doi.org/10.7575/aiac.ijalel.v.10n.1p.80 Language choices and its effect in a culturally diversified nigeria business places: Adopting giles’ communication accommodation theory . International Journal of Applied Linguistics and English Literature

work page doi:10.7575/aiac.ijalel.v.10n.1p.80 2021

[4] [4]

Brau, James Cicon, and Stephen R

James C. Brau, James Cicon, and Stephen R. Owen. 2022. https://doi.org/10.3390/ijfs10020025 A textual analysis of logograms in chinese ipo roadshows: How agreement between investors and management relates to pricing and performance . International Journal of Financial Studies, 10(2):25

work page doi:10.3390/ijfs10020025 2022

[5] [5]

Brodersen, C.S

K.H. Brodersen, C.S. Ong, K.E. Stephan, and J.M. Buhmann. 2010. The balanced accuracy and its posterior distribution. In Proceedings of the 20th International Conference on Pattern Recognition, pages 3121--3124

work page 2010

[6] [6]

Sandra Cornbleet and Ronald Carter. 2002. The Language of Speech and Writing. Routledge. Creative Commons license (2001). Available at http://ndl.ethernet.edu.et/bitstream/123456789/8674/1/46.pdf.pdf

arXiv 2001

[7] [7]

Cristian Danescu-Niculescu-Mizil and Lillian Lee. 2011. Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011

2011

[8] [8]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 Bert: Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and...

doi:10.18653/v1/n19-1423 2019