The article focuses on the systemic efects of censorship that manifest themselves in the content of published materials that successfully passed the censorship filters. We understand censorship as a special kind of collective imagination about the (in)acceptable, inherent in a particular political context and influencing the decision-making logic by diferent actors. The idea is that censorship afects the ability of the authors to navigate the topical space, so that juxtaposition of certain topics (e.g. literature and politics) is specifically avoided. To detect this efect, we suggest an idea of topical dissociation, operationalized as a probability that either one or the other topic appear in the same articlen,botubtoth. We apply LDA topic modeling to the corpus of Russian literary magazine Sovremennik (1847-1866) to trace topic dissociation across the period. We hypothesize how the strength of topical dissociation should change with respect to the historical data on the changing censorship practices of the period. Empirical data only partially supported our hypotheses. The method has a potential for wider application to study censorship efects on the published materials.
Censorship is a phenomenon that afected availability and content of much published material in diferent countries in modern history. To better understand the composition of our corpora, it is important to detect and measure the efects of censorship in texts.
The current consensus in humanities converges on understanding censorship not just as a
repressive apparatus of the government, but rather as a special kind of collective imagination
about the (in)acceptable [
Our central idea is that censorship afects the ability of the authors to navigate the
topical space, so that juxtaposition of certain topics (e.g. literature and politics) are specifically
avoided. The ecological validity of this idea is supported by the well-known historical cases
when direct and sometimes unconscious proximity of certain articles, themes or ideas could
and was interpreted by censors as an intentional connection of meanings, the establishment of
a causal relationship between them (see a thorough review of diferent censorship case1s1[]
or one of the most well-known books on the history of Imperial censorship and the Russian
press [
To measure the avoidance of topic juxtaposition we suggest the notion of topical dissociation, operationalized as a probability that either one or the other topic appear in the same article, but not both. We expect that the higher the censorship pressure, the higher the dissociation for some pair of politically sensitive topics would be, even when both topics are discussed in legitimate (censorship-wise) ways.
In this paper we present an empirical study that tests this idea on the corpus of theth19 century Russian literary magazine Sovremennik (The Contemporary) (1847–1866). During this period censorship policies changed significantly, so that we can hypothesize about the timing and direction of the censorship efect on topical dissociation.
To identify the systemic efects of censorship, we turn to the materials of Sovremennik, one of the most influential Russian literary magazines of the period. This magazine, by the middle of the 19th century, received exceptional recognition from the readers and absorbed most of the contemporary fiction works that later became canonical. As such, it was highly visible to the government, and fully experienced censorship practices of the period.
The history of Sovremennik spans a few institutional and ideological changes in respect to
censorship in Russia 5[
It is also known that diferent periods in the history of Sovremennik vary in terms of editorial
policy [
From January 1847, Sovremennik was under the editorship of Nikolay Nekrasov and Ivan
Panaev. The end of the first editorial period corresponds with the cancellation of the so-called
binding agreement with Ivan Turgenev and other leading writers of liberal views who started
leaving the magazine in March 1858 1[
Between April 1858 and May 1862, Sovremennik was led by the authors of social-democratic views with Nikolay Chernyshevsky and Nikolay Dobrolyubov in the forefront. The period ends with the suspension of publishing activities for Sovremennik.
Finally, from January 1863 to May 1866, Sovremennik resumed its activities, but Dobrolyubov by the time was already dead (November 1861), and Chernyshevsky, arrested in July 1862, was still detained.
Mikhail Saltykov-Shchedrin took a lead role, but the final closure of the magazine was not long in coming. It followed after the first attempt on Alexander II’s life that caused ofÏcial panic and tough reactionary policy.
During the period of Sovremennik, literature was one of the mediums where the formation of public opinion that is independent of the government took plac1e].[ Our test case is based on the expectation that censorship pressure would cause authors and editors of Sovremennik to avoid language related to politically sensitive topics while discussing literary subjects. At the same time, the relative strength of censorship pressure difered within the three censorship regimes listed in the previous section. Summarising historical changes in a single dimension of censorship strength we would say it was at its highest in the first period (1847–1855), then hit the lowest point after the death of Nikolai I (1855–1862) to be partially reinstated in the third period though not attaining its initial strength. Hence, we hypothesize that the strength of dissociation of literary and political topics in Sovremennik would follow this trajectory of censorship pressure (see figure1).
As a control condition, we include a test for dissociation of literary and non-political topics that should not be sensitive to changes in the censorship pressure. For this, we use topics of theatre and fine arts that were also prominent in the content of Sovremennik. We base our topical dissociation measurements on a LDA topic model of a comprehensive digital corpus of Sovremmenik’s publications.
The aim of our analysis is the direct causal efect of the censorship regime on topic dissociation, i.e. self-censorship by the authors realized as avoidance of topical juxtaposition. Due attention should be paid to possible confounders. One alternative causal path would involve editorial policy that is both afected by a censorship regime and influences the topical composition of the corpus through selection of articles and direct editing. The use of LDA for operationalizing a topic’s presence in a document produces a strong negative association between document length and topical dissociation (a longer document contains more LDA topics that surpass a certain minimal word number threshold, for our model the correla ti=on0.i9s1 ). This creates another causal path since article length also depends on editorial policy. Finally, it is reasonable to assume the existence of unmeasured confounders that afect both document length and editorial policy, for instance, a fashion for longer literary texts or economic considerations of the publisher. These assumptions are summarized in a DAG in the figur2e. Given this DAG, to estimate the direct causal efect of censorship on topic dissociation one needs to stratify the data by editorial policy and article length.
In this paper we focus on the whole corpus of Sovremennik publications, including fiction, scientific and critical articles from the magazine’s program sections, but also numerous essays and short stories by minor authors, news items, chronicles, reviews, and other materials. We used a digitized version of this corpus19[]. In total, 5412 materials were published in Sovremennik (excluding appendices to the magazine) between 1847 and 1866. The corpus includes texts for 4686 of them (87%). Since the quality of the originals varied and the OCR results were not corrected, the data quality is inferior for some documents. In total, 16,4% of tokens were identified as non-words (most likely OCR errors).
Data and code to replicate the analysis is available a2t0[].
Topical dissociation for a pair of topics is defined for a corpus where either of the topics may occur in any given document. The strength of topical dissociation is expressed by the probability that either one or the other topic occur in a document, bnuot both. A direct measure for the share of such documents in a corpus is a well-known Jaccard distance = 1 − ( 1, 2) = 1 − | 1 ∩ 2| | 1 ∪ 2| = | 1 ∪ 2| − | 1 ∩ 2| | 1 ∪ 2| (1) where 1 and 2 stand for the sets of documents which contain the first and the second topic, respectively. Importantly, by this definition the documents where neither of the topics occur do not attest for or against the topical dissociation, and should be regarded as irrelevant to the measurement. In our case, a single publication in Sovremennik (an article) serves as a document.
The limitation of the Jaccard distance is that it gives a single point estimate for the whole corpus. To properly account for the uncertainty of estimation and document-level confounders we need a way to define the probability of topical dissociation for a single document. This is achieved by recognizing that Jaccard distance essentially aggregates a set of binary outcomes for every document in a corpus = ⎧1
0 ∈ 1 ∩ 2 ⎨ ⎩ undefined ∉ 1 ∪ 2
∈ 1△ 2 = ∈ 1 ∪ 2 ∧ ∉ 1 ∩ 2
Every document-level outcome is interpreted here as a Bernoulli trial for which a conditional probability model can be defined. Then topical dissociation for a pair of topi1csand 2 is given by Δ ( 1, 2) = ( |
), for ∈ 1 ∪ 2 where ( | ) is the probability that a document occurs in a topical dissociation set, and is a vector of parameters that describe document-level predictors (article length, editorial policy, censorship regime). To estimate the topical dissociation probability we apply a Bayesian generalized linear model (logistic regression). All posterior distributions are estimated using STAN’s Hamiltonian Monte Carlo algorithm. More details on the definition of the statistical model can be found in the appendixA.
For LDA modeling all text was converted into modern orthography and lemmatized with automatic disambiguation. All text was split into chunks of 500 tokens or less in case of shorter articles. Only nominal and verbal lemmas occurring in less than 50% of chunks were kept for modeling. For interpretation, we selected a model with 200 topics. The topic was considered (2) (3) to occur in an article when its share exeeded a threshold corresponding to a 1/3 of a journal page.
To measure dissociation, we selected three sets of topics from the LDA model. The first set (11 topics) represents the discussion of literary subjects, including journalistic polemics, writing manner, literary talent, poetry, book distribution, etc. The second set (16 topics) represent politically sensitive topics important for public opinion at the time: government (state), peasants, public life, nation, politics, justice. The third set (4 topics) serves as a control condition and represents discussion of theatre and fine arts. In these sets, we included only the topics relevant throughout the magazine’s entire publication period, omitting those topics that were mostly localized in a short period of time. The summary of the topics is given in the appenBd. ix
Summaries of the posterior probability distributions of the topic dissociation for our focal (literature vs. politics) and control (literature vs. art/theatre) conditions are displayed in 3fig.ure Data points correspond to all possible combinations of censorship regime and editorial policy.
As expected, the dissociation between literature and politics is highest in the first censorship regime. During the second censorship regime (1855–1862) Sovremennik has undergone changes in editorial team and policy, therefore we observe two separate states here. After the death of Nikolai I and still under the leadership of Nekrasov and Panaev the average expected dissociation dropped by 10 percentage points. This efect can be attributed to the diminishing censorship pressure. The editorial board led by Chernyshevsky and Dobrolyubov was inclined to include public issues in literary discourse more than the previous generation of authors as they believed that literature could change reality by presenting positive ideas and models for progress. The observed topical dissociation diminished accordingly (11 percentage points) even during the same censorship regime. Contrary to our expectations, more regulated censorship policy during the third regime did not result in growth in topical dissociation for literature and politics.
While we expected more or less constant dissociation rates for our control condition (literature vs. art/theatre), the data proved us wrong. Instead, we observe the trend that is opposite to the dissociation of literature and politics. It appears that the probability of dissociation steadily increases over time, across all censorship periods. Thus the topical pair of literature and arts displays an efect of censorship that is similar in strength though inverse in direction. If anything, it means that the dissociation of literary topics from the other arts was both an efect of diminishing censorship pressure and part of the Sovremennik’s editorial policy after 1858.
The conceptual simplicity of the suggested measure for topic dissociation (which is essentially an enhanced version of Jaccard distance) is both a strength and a weakness. One important concern may be raised when we compare topical dissociation between two periods. Our measure ignores the diference in overall corpus size and keeps track only of the overlap of two topics relative to each other. In our view, the possible change in the coverage of other topics should not afect the dissociation measure for the two focal topics if their relative overlap remains the same. Moreover, since the size of our data corpus is not balanced across time periods, any dependence of topic dissociation on corpus size would be undesirable. Hence, we prefer our definition to measures of association that depend on corpus size, notably, to pointwise mutual information as widely applied in NLP for measuring co-occurrence strength.
Another debatable decision we took was to binarize topic presence in a document using 1/3
page threshold (see appendixB for details). An alternative approach would be to use LDA
topic probabilities for each document to estimate topic association, as suggested, for instance,
in definition of topic linkage in [
The definition of topical dissociation that we suggest allowed us to trace separate efects of the changing strength of censorship pressure and the editorial team’s policy during the publishing period of the Sovremennik magazine.
The empirical results only partially supported our initial hypotheses. While the drop of the topical dissociation after the first period is in line with the expectation of the lowered censorship pressure, there was no rise of dissociation in the third period when the censorship policy has been updated again. One possible explanation is that the preparation and implementation of the 1863–1865 censorship reform primarily involved institutional change (reorganization of censorship departments), but did not imply stricter content censorship policy. Institutional changes appear to have had a minimal impact on the topical dissociation.
Moreover, the data contradicts our assumption that topical dissociation of literature and
theatre/arts should not be sensitive to changes in the censorship regime. Apparently,
depolitization of literature and placement of the literary discourse into the topical space of arts was
characteristic of censorship and might be one of the efects of higher censorship pressure.
During the last years of Nikolai I’s reign, the censorship committee found even the most innocent
things objectionable. According to one of the authors of this time period, the censors referred
to the censorship committee as a scarecrow, who threatens punishment for every printed word
[
In our research, we aimed not only to understand a specific case from the history of Russian press in the 19th century, but also to identify general patterns of how censorship influenced journalism. Our results suggest that censorship pressure may afect the topical dissociation in some predictable way. But it also showed that censorship efect on the published content can be decoupled from the institutional changes in the censorship procedures.
On a more general level, we would like to further the theoretical reflection on what
censorship is and how it can be operationalized. By defining censorship as a dissociative force, we
take into account, on the one hand, the liberal understanding of censorship as an instrument of
the state to limit the freedom of individuals. On the other hand, we consider the more recent
research that have challenged the traditional binary opposition between power and free speech,
or truth, that is assumed by the model of censorship as a governmental limitation of freedom.
In the latter view, power is not seen as a form of limiting the truth, but rather as a monopoly
over it, and censorship serves as an expression of that trut9h, [
of Toronto Press, 1982. [11] [12] History of Russian Censorship and Journalism of the 19th century]. Printing house of the Trud Partnership, 1904.
M. Magnusson and D. Mimno.mallet: An R Wrapper for the Java Mallet Topic Modeling Toolkit. 2022. url: https://CRAN.R-project.org/package=malle.t M. Makeev. Nikolay Nekrasov. Young Guard, 2017.
A. Nikitenko. Dnevnik [Diary]. State Publishing House, 1955.
[
House of the Saratov Pedagogical Institute, 2000. it-2023.11-C006. [To Educate and to Punish: The Functions of Censorship in the Russian Empire in the Middle of the 19th century]. New Literary Review, 2023.
The formal definition of the statistical model is given below i4n.
Δ ( , ′ ) ∼ Binomial(1, ) logit( ) = , + log( ) ,
∼ Normal(0, 1) ∼ Normal(0, 1), (4) ′ ′ whereΔ ( , event. It equals 1 when either or is present in the document, and 0 when both are present.
) is a dissociation bet′ween topic s and in an article . Δ is considered a binary The topic is considered “present” in the document if its share in the document corresponds to at least 1/3 of the magazine page or more. The documents where neither topic is present are considered irrelevant for the measurement of the topic dissociation and are excluded from the data. stands for the expected probability of dissociation for in artic,laend is the standardized log-length of an artic lein pages. Efects of censorship regime and editorial policy are captured by , . A separate coefÏcient is computed for each attested combination of the censorship regime and editorial policy (4 in total).
The LDA topic inference was performed using R wrapper for MALLET packag1e2[]. We were choosing between topic models with 100, 200, and 300 topics. All these models had the same beta hyperparameter valu e = 0.01
while alpha parameters difered: 0.05, 0.025, and 0.0167, respectively. The model wit h= 200 was selected for analysis due to comparatively greater interpretability of the topics (45 topics were hard to interpret).
For our analysis, we selected only the topics relevant to literature, arts, and politics, and grouped them into three corresponding categories. The category was considered to be present in an article if any topic from that category in an article exceeded word limit corresponding to a 1/3 of a journal page. The top 10 words for the topics in each category are listed below. literature 18th and 19th century writers, literary reputation, periodicals, literary criticism, genres, poetry and prose, versification, literary language 1. article (stat’ya), magazine (zhurnal)S,ovremennik (sovremennik), newspaper (gazeta), Russian (russkij), reader (chitatel’), year (god), new (novyj), literature (literatura), editorial (dikkens) French (francuzskij) board (redakciya) (stopa) 2. great (velikij), name (imya), writer (pisatel’), literary (literaturnyj), poet (poet), fame (slava), genius (genij), work (proizvedenie), talent (talant), famous (znamenityj) 3. hexameter (gekzametr), meter (razmer), Nikolay Gnedich (gnedich), vocable (vokabula), accent (udarenie), dactyl (daktil’), iambic (yamb), spondee (spondej), caesura (cezura), feet 4. Alexander Pushkin (pushkin), year (god), Nikolay Gogol (gogol’), Russian (russkij), literary (literaturnyj), Anton Delvig (del’vig), literature (literatura), Nikolay Karamzin (karamzin), poet (poet), Vasily Zhukovky (zhukovskij) 5. satire (satira), Antiochus Kantemir (kantemir), satirist (satirik), satirical (satiricheskij), vice (porok), denunciation (oblichenie), Juvenile (yuvenalV)s,yakaya vsyachina (vsyachina), Zhivopisets (zhivopisec), Nikolay Novikov (novikov) 6. author (avtor), work (proizvedenie), short story (rasskaz), reader (chitatel’), character (lico), story (povest’), talent (talant), character (harakter), life (zhizn’), content (soderzhanie) 7. verse (stih), poet (poet), short poem (stihotvorenie), poetry (poeziya), song (pesnya), poetic (poeticheskij), poem (poema), kind of literature (rod), prose (proza), Afanasy Fet (fet) 8. novel (roman), story (povest’), reader (chitatel’), author (avtor), hero (geroj), literature (literatura), novelist (romanist), short story (rasskaz), kind of literature (rod), Charles Dickens 9. word (slovo), language (yazyk), Russian (russkij), author (avtor), translation (perevod), book (kniga), French (francuzskij), place (mesto), reader (chitatel’), expression (vyrazhenie) 10. literature (literatura), criticism (kritika), William Shakespeare (shekspir), art (iskusstvo), literature (slovesnost’), writer (pisatel’), work (proizvedenie), poetry (poeziya), poet (poet), art fine arts, theatre, music 1. comedy (komediya), stage (scena), play (p’esa), drama (drama), theater (teatr), dramatic (dramaticheskij), actor (akter), action (dejstvie), tragedy (tragediya), character (lico) 2. painting (kartina), art (iskusstvo), artist (hudozhnik), work (proizvedenie), portrait (portret), exhibition (vystavka), painting (zhivopis’), drawing (risunok), work (rabota), painter (zhivopisec) 3. theater (teatr), public (publika), stage (scena), role (rol’), opera (opera), new (novyj), performance (predstavlenie), artist (artist), great (bol’shoj), success (uspekh) 4. music (muzyka), musical (muzykal’nyj), concert (koncert), voice (golos), opera (opera), singing (penie), sound (zvuk), choir (hor), orchestra (orkestr), instrument (instrument) politics justice (branches of law, judicial proceedings, punishment), nation (origin of the nation, definition of the nation), peasants (peasant question, serfdom, emancipation reform), political institutions, parties, power (state and government, regulation process, imperial administration), public (public associations, non-state groups, publicity) 1. law (pravo), law (zakon), property (imushchestvo), person (lico), property (sobstvennost’), marriage (brak), child (rebenok), legal (zakonnyj), inheritance (nasledstvo), relative (rodstvennik) 2. punishment (nakazanie), crime (prestuplenie), prison (tyur’ma), law (zakon), criminal (prestupnik), imprisonment (zaklyuchenie), execution (kazn’), court (sud), strict (strogij), sentence (prigovor) 3. court (sud), case (delo), investigation (sledstvie), crime (prestuplenie), judge (sud’ya), witness (svidetel’), testimony (pokazanie), trial (process), person (lico), defendant (podsudimyj) 4. case (delo), court (sud), law (zakon), judicial (sudebnyj), judge (sud’ya), law (pravo), decision (reshenie), person (lico), case (sluchaj), police (policiya) 5. convict (arestant), Siberia (sibir’), exiled (ssyl’nyj), work (rabota), colony (koloniya), vagrant (brodyaga), settlement (poselenie), factory (zavod), convict (katorzhnyj), prison (ostrog) 6. Russian (russkij), people (narod), folk (narodnyj), ancient (drevnij), Slav (slavyanin), Slavic (slavyanskij), Rus (rus’), Russia (rossiya), tribe (plemya), nationality (narodnost’) 7. peasant (krest’yanin), land (zemlya), landowner (pomeshchik), peasant (krest’yanskij), owner (vladelec), serf (krepostnoj), estate (imenie), plot of land (uchastok), right (pravo), obligation (povinnost’) 8. peasant (muzhik), master (barin), landowner (pomeshchik), peasant (krest’yanin), elder (starosta), village (derevnya), lawman (ispravnik), mediator (posrednik), liberty (volya), master (gospodin) 9. household (hozyajstvo), rural (sel’skij), land (zemlya), bread (hleb), field (pole), cattle (skot), good (horoshij), landlord (hozyain), big (bol’shoj), farming (zemledelie) 10. party (partiya), side (storona), case (delo), political (politicheskij), struggle (bor’ba), force (sila), last (poslednij), movement (dvizhenie), new (novyj), opponent (protivnik) 11. chamber (palata), government (pravitel’stvo), minister (ministr), deputy (deputat), speech (rech’), ministry (ministerstvo), vote (golos), assembly (sobranie), election (vybory), member (chlen) 12. ofÏcial (chinovnik), service (sluzhba), position (mesto), governor (gubernator), chief (nachal’nik), excellency (prevoskhoditel’stvo), general (general), position (dolzhnost’), secretary (sekretar’), provincial (gubernskij)
Following our operationalization of a presence of a topic in an arti≥cl1e/(3 page) we can define what proportion of articles in each period contains discussion of our major topical categories: literature, arts, and politics. The proportions are shown in the figu4r.e The data displays a clear upward trend for political topics while the share of the Sovremennik space devoted to literary topics remained remarkably stable throughout its history. Arts, on the contrary, were on the steady decline.