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REVIEW 3 major objections 5 minor 54 references

A political cartography of news sharing: Capturing story, outlet and content level of news circulation on Twitter

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Two political dimensions reveal news-sharing splits hidden from a left-right-only view.

desk verdict A useful integrated pipeline, but the proof-of-concept evidence is more illustrative than systematic; the confirmatory hypotheses outrun the selected examples. read the letter →

arxiv 2505.08359 v1 pith:ZHUAHEUD submitted 2025-05-13 cs.SI

Felix Gaisbauer , Armin Pournaki , Jakob Ohme This is my paper · ORCID
classification cs.SI
keywords newssharingnetworkembeddingstopicmodelsdigitaltracedatasocialmediaTwittertwo-dimensionalpoliticalspacecartography
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 argues that studying who shares which news on social media needs three things at once: a political space with more than one dimension, a view of individual articles rather than only outlets, and a content taxonomy built from the texts themselves. It builds a pipeline that places roughly 1.2 million German Twitter users in a two-dimensional political space inferred from the MPs they follow, then maps about 500,000 March 2023 shares of articles from 26 outlets onto that space, with article full texts grouped into 220 topics and 12 metatopics. The proof of concept shows patterns invisible in a one-dimensional outlet-only design: outlets like focus.de and nachdenkseiten.de look alike at outlet level but differ sharply at story level, and the same outlet can be shared by different camps for different topics. If the pipeline holds up, it gives researchers a generalizable way to measure news circulation by source and content, not just by source.

What carries the argument

The load-bearing object is the CHES-validated two-dimensional political space. It starts from the follower network of 433 German MPs: a correspondence analysis embeds 1.2 million users who follow at least three MPs, and the axes are rotated until they align with party positions from the Chapel Hill Expert Survey, yielding a left-right x-axis and a y-axis combining elite-skepticism, EU-skepticism, internal-market position, and protectionism (the EESP axis). Around this space, the pipeline layers link-level sharing data, roughly 500,000 tweets from March 2023 across 26 outlets, full texts scraped with newspaper3k, and a 220-topic Structural Topic Model aggregated into 12 metatopics. The key analytic move is comparing distributions at three granularities: users per outlet, users per individual story, and users per topic or metatopic, which is what lets the paper separate "shared by both sides" from "different sides share different items from the same outlet."

What would settle it

A validation study that matches a sample of embedded users to survey responses on left-right and EU/protectionism attitudes and checks whether their embedding coordinates predict reported attitudes would settle the question; if the agreement is weak or the second axis fails to discriminate, the pipeline's political map is not measuring what it claims.

Watch

Extended reading notes

Core claim

The central claim is that a two-dimensional political space with story- and topic-level resolution reveals systematic news circulation patterns that one-dimensional, outlet-level analyses miss. The paper confirms this with a German proof of concept: H1, H2a, H2b, and H3 are all answered positively. Concretely, the AfD occupies its own region high on the elite-/EU-skeptical/protectionist axis; outlets that look similar on the left-right axis (faz.net versus welt.de) separate on the second axis; focus.de and nachdenkseiten.de have similar outlet-level sharing distributions but different story-level structures, with focus.de's stories rarely bridging the EESP cleavage and nachdenkseiten.de's stories often shared across it; and climate-change news circulates across the spectrum while right-wing EESP users share stories about climate institutions while left-of-centre users share extreme-weather stories. The paper presents these as proof that the pipeline can uncover systematic insights in news sharing.

Load-bearing premise

Everything rests on the assumption that users follow politicians they agree with; if following on Twitter is driven by fame, novelty, or social reasons, the political coordinates of every user, outlet, story, and topic shift.

Editorial extensions

If this is right

  • If the pipeline is adopted beyond Germany, any country covered by CHES party-position data can get the same two-dimensional treatment, and the method also allows more than two dimensions in principle.
  • Story-level distributions become a direct measure of news unbundling: an outlet whose story means straddle a cleavage is supplying different camps with different items, while an outlet whose story means sit between poles is producing genuinely cross-cutting content.
  • Topic-by-outlet comparisons expose selective exposure at the level of beats, for example right-wing EESP users ignoring focus.de's climate coverage while sharing welt.de's, which outlet-level averages flatten.
  • The metatopic-versus-topic distinction provides a bottom-up way to detect subtler forms of biased information environments: the same broad issue is shared by all camps, but systematically different aspects circulate in each camp.
  • Combining follower-based ideology with text-based content lets researchers study circulation without pre-labelling outlets as partisan or non-partisan and without pre-judging articles as true or false.

Reading between the lines

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

  • A natural next test is to run the same pipeline on a second month or another country; if the EESP axis does not reproduce, the German result may be specific to the timing or the particular MP list rather than to the method.
  • The 60 percent embedding coverage means the untraced 40 percent of shares could come from users who follow fewer politicians and are systematically different in age, tenure, or engagement; comparing those users' aggregate sharing with embedded users' sharing would bound this bias.
  • Because the second axis rests on excluding CA dimension 2 on the grounds that it tracks MP follower counts, re-running the pipeline with that dimension included, or with popularity partialled out, would show whether the EESP findings survive.
  • Story-level analysis could be converted into a quantitative "unbundling score": the fraction of an outlet's stories whose mean sharing position falls on one side of a cleavage, a metric that could be compared across platforms and over time.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces a methodological pipeline that combines correspondence analysis (CA) of German MPs' Twitter follower networks with a CHES-based rotation to construct a two-dimensional political space, and then maps news sharing of 26 outlets using tweets from March 2023, article full texts, and a structural topic model with 220 topics grouped into 12 metatopics. The authors claim that this pipeline reveals systematic insights unobservable in one-dimensional, outlet-only designs, and they report confirmations of four hypotheses (H1, H2a, H2b, H3) on the basis of selected visual examples: three outlets in Fig. 2, two articles and two outlets in Fig. 3, two outlets across two metatopics in Fig. 4, and four topics within one metatopic in Fig. 5. The paper frames itself as a proof-of-concept methodological contribution, with an accompanying repository for the CA and rotation code.

Significance. If fully substantiated, the pipeline is a valuable advance: it offers a multi-dimensional, multi-level approach to studying news circulation, extends network-embedding ideology estimation beyond a single left-right axis, and foregrounds story- and topic-level heterogeneity in a way that most prior work has missed. The manuscript is transparent about the embedding and rotation steps, provides code, and includes detailed appendices on data collection, topic model validation, and metatopic coding. The main weakness is that the confirmatory evidence for all four hypotheses is anecdotal: selected cases are used where systematic, population-level statistics or null-model comparisons across all outlets, stories, and topics are needed. As a proof of concept the paper has merit, but its central claim of 'systematic insights' currently rests on hand-picked examples rather than on a quantitative demonstration that the pipeline generalizes.

major comments (3)
  1. [Section 4.1, Fig. 2] The confirmation of H1 is based on the visual comparison of only three outlets (sueddeutsche.de, faz.net, welt.de), with no summary statistic over the full set of 26 outlets. To establish 'systematic insights' rather than anecdotal differences, the authors should quantify the divergence between outlet sharing distributions in the one- and two-dimensional spaces across all outlets (e.g., compute an overlap or distance metric in each space and compare the distributions of those metrics, with uncertainty estimates). Without such an analysis, the claim that the two-dimensional space reveals systematic patterns is not supported by the evidence presented.
  2. [Sections 4.2.1, 4.2.2, and 4.3 (Figs. 3–5)] H2a, H2b, and H3 are each confirmed on the basis of a small number of hand-picked examples: two articles and two outlets for H2a, two outlets and two metatopics for H2b, and four topics within a single metatopic for H3. Because the data comprise 26 outlets, 220 topics, and 12 metatopics, the selected cases could be extreme draws, and no criterion is given for what would count as 'systematic'. The authors should provide population-level evidence, such as (i) the distribution of story-level mean sharing positions for all outlets along with a heterogeneity measure (e.g., the share of stories whose mean position falls outside a central interval), and (ii) a test, for all topics or metatopics, of whether topic-level sharing distributions differ significantly from the outlet-level distribution (e.g., via permutation tests or a hierarchical model). Until such quantification is supplied, the confirmatory language in Sections 4.2 and 4.3 overstates what the figures establish.
  3. [Section 3.1 and Appendix C] Two data-dependent choices shape all subsequent results: the exclusion of CA dimension 2 (justified by a Spearman correlation of 0.79 with MP follower counts) and the CHES rotation procedure (a correlation cutoff of 0.8, with a second axis built by averaging over four rotation angles within a 22-degree window). These choices are plausible but are presented without sensitivity analyses. To make the pipeline a robust methodological contribution, the authors should demonstrate that the main findings persist (i) when dimension 2 is included or a popularity-adjusted embedding is used, (ii) when the CHES cutoff is varied around 0.8, and (iii) when the second axis is derived from a single best-fitting CHES issue rather than the average of four. In addition, because the rotation is optimized to maximize correlation with the CHES issues, the reported correlations of about 0.9 should be framed as the outcome of an optimization, not as independent validation; an out-of-sample or held-out validation would strengthen the interpretation of the EESP axis.
minor comments (5)
  1. [Section 2.1] There are several typographical errors in this section, including 'frequeny' (should be 'frequency'), 'contries' (should be 'countries'), 'expample' (should be 'example'), and 'spce' (should be 'space').
  2. [Fig. 1 caption] The caption contains a typo: 'othogonal' should be 'orthogonal'.
  3. [Section 3.1] The overview states 'we use two Principal Components to construct a two-dimensional space', but the procedure later explains that the authors operate with PCs 1 and 3, excluding PC 2. The overview should be clarified to avoid implying that PCs 1 and 2 are used.
  4. [Section 3.1 and Section 3.2] The text says 'nearly 1.2 million users' are embedded, while the sample description says '1.2 million followers being included in our pipeline.' Clarify whether this refers to users or followers, and whether the 60% coverage of shares refers to unique users or shares.
  5. [Section 5, Discussion] The sentence 'right-leaning focus.de was generally shared often by right to right-wing users' is awkwardly phrased; consider revising for clarity.

Circularity Check

1 steps flagged · score 2.0 of 10

One minor circularity: the CHES-aligned axis is called 'validated' although it was fitted to CHES; the main news-sharing results are independent.

  1. self definitional [Section 3.1, 'Procedure' (axis construction) and Section 5, Discussion, first paragraph]
    "We then rotate the embedding degree-wise and calculate correlations with the two dimensions. Once we reach a maximum in the sum of correlations along the x- and y-axis, this pair of CHES issues helps us interpret x- and y-axis. ... An estimation of users’ political positions in a CHES-validated, two-dimensional political space, based on follower network embeddings"

    The second political axis is not independently validated by CHES; it is constructed by rotating the CA embedding so that party-average positions maximize correlation with CHES scales (left-right plus elite-skepticism, EU position, internal market, protectionism). The reported correlations above 0.9 are optimization outcomes, not external confirmation. Labeling the resulting space 'CHES-validated' presents the fitting criterion as if it were validation. This circularity affects only the interpretive label of the axes; the outlet-, story-, and topic-level sharing distributions are computed from tweet data placed in the already-fitted space, so the central empirical claims do not reduce to this step.

full rationale

The paper's central claims concern news-sharing patterns across an embedding space, and those patterns come from external tweet data: outlet sharing distributions, story-level means, and topic/metatopic distributions are all measured from March 2023 Twitter shares, not derived from the embedding construction. H1, H2a, H2b, and H3 are demonstrated with visual exemplars rather than formal statistics, but that is an evidentiary weakness, not circularity. The self-citations to Ramaciotti Morales et al. (2022) and Gaisbauer et al. (2023) describe methods or intuitions that are re-implemented and tied to CHES data; they are not invoked as unexamined uniqueness theorems. The one genuine circular step is the 'CHES-validated' label applied to an axis whose rotation was chosen to maximize correlation with CHES. Because this overstatement does not feed the news-sharing results and the paper explicitly treats the political positioning as the foundation rather than the result, the overall circularity is minor.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The pipeline does not introduce new physical entities. Its main constructed entity is the EESP axis, which is anchored externally in CHES but selected by maximizing correlation with the same scales. The free parameters are mostly threshold and selection choices that shape the embedding and content assignments; they are disclosed, but several are boundary-sensitive.

free parameters (5)
  • Topic probability threshold for main topic assignment = 0.5
    Chosen in Appendix H because the histogram of maximum topic probabilities peaks around 0.5; used to assign each article a main topic or metatopic for content-level analyses.
  • Number of STM topics = 220
    Selected by varying the number from 20 to 300 and manually examining models at 80, 100, and 220; 220 gave the best tradeoff between coherence and granularity.
  • CHES correlation cutoff = 0.8
    Used to decide which CHES issues are represented by the second axis; the next candidate, anti-Islam rhetoric at 0.79, is excluded, making the choice boundary-sensitive.
  • Minimum MP follow count for embedding inclusion = 3
    Users following fewer than three MPs are excluded; the value is taken from prior work, but it is a data-dependent inclusion threshold that affects the embedding sample.
  • CA dimension selection = PCs 1 and 3, dropping PC2
    PC2 correlates with MP follower counts (Spearman 0.79), so the authors discard it and use PCs 1 and 3. This selection affects which variation the political axes explain.
assumptions (4)
  • domain assumption Users' following of politicians is a signal of ideological proximity (ideological homophily).
    Invoked in Section 3.1 as the fundamental assumption of the political embedding; if false, MP positions and user positions do not reflect ideology.
  • domain assumption CHES expert survey positions of German parties are valid reference points for interpreting the embedding axes.
    Used in Section 3.1 to rotate the CA space and label the second dimension; wrong expert positions would mislabel the axes.
  • ad hoc to paper The excluded second CA dimension mainly reflects MP popularity rather than political meaning.
    The authors infer this from the correlation with follower counts and party variance in Section 3.1, but no formal decomposition is given. The two-dimensional space and all subsequent claims depend on this exclusion.
  • domain assumption The structural topic model with 220 topics provides a valid representation of article content for metatopic grouping.
    Content-level results in Sections 4.2.2 and 4.3 rely on STM probabilities and hand-coded metatopic labels; topic model errors propagate into sharing distributions.
invented entities (1)
  • Elite-/EU-skeptical protectionist (EESP) axis independent evidence
    purpose: Second political dimension used to map news sharing by source and content.
    The axis is constructed by rotating the CA embedding to align with four CHES issue scales, so CHES provides an external anchor. However, because the rotation is optimized against those same scales, the alignment is partly by construction rather than an independent confirmation.

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Pith. "Pith review of A political cartography of news sharing: Capturing story, outlet and content level of news circulation on Twitter." pith.science (2026). https://pith.science/paper/ZHUAHEUD

@misc{pith2026250508359,
  author       = {Pith},
  title        = {Pith review of: A political cartography of news sharing: Capturing story, outlet and content level of news circulation on Twitter},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZHUAHEUD}},
  note         = {Machine review of arXiv:2505.08359}
}
read the original abstract

News sharing on digital platforms shapes the digital spaces millions of users navigate. Trace data from these platforms also enables researchers to study online news circulation. In this context, research on the types of news shared by users of differential political leaning has received considerable attention. We argue that most existing approaches (i) rely on an overly simplified measurement of political leaning, (ii) consider only the outlet level in their analyses, and/or (iii) study news circulation among partisans by making ex-ante distinctions between partisan and non-partisan news. In this methodological contribution, we introduce a research pipeline that allows a systematic mapping of news sharing both with respect to source and content. As a proof of concept, we demonstrate insights that otherwise remain unnoticed: Diversification of news sharing along the second political dimension; topic-dependent sharing of outlets; some outlets catering different items to different audiences.

Figures

Figures reproduced from arXiv: 2505.08359 by the authors.

Figure 1
Figure 1. The process of identifying interpretable political axes from a two-dimensional space derived from Correspondence Analysis (CA). Left panel: Initial two-dimensional space that includes MPs represented by colored dots. Their colors are corresponding to their political party (i.e., AfD blue, Die Linke pink, Greens green, CDU/CSU black, FDP yellow, SPD red). The green density plot shows user distribution across this spa… view at source ↗
Figure 3
Figure 3. Circulation of individual news stories (A., B.), outlet-level distributions of two outlets (C., E.) and distribution of story means (D., F.). Two articles from focus.de are shared in different regions of the political space (individual shares are displayed as blue crosses, the mean of all shares of the respective story by a red ‘X’). If we investigate the distribution of users sharing focus.de articles (regions of h… view at source ↗
Figure 4
Figure 4. Comparison of the sharing distributions of welt.de and focus.de for different metatopics. While articles on environmental issues and climate change by the latter outlet are shared comparatively more often by users left of center (B.), its coverage on national politics is shared by right-leaning users both high and low on the EESP axis (A.). welt.de articles on national politics (C.) are very similarly distributed to… view at source ↗
Figures from the paper (8 more)
Figure 5
Figure 5. Figure 5: Four topics and their corresponding metatopic (environment and climate change). News on environment and climate change are shared by users across the political spectrum. Yet, stories on extreme weather events and Fridays For Future are shared nearly only left of center…
Figure 6
Figure 6. Figure 6: CA percent of variance per dimension. The follower network can be described as a adjacency matrix A with NMP columns and Nusers rows. An entry aij is 1 if a user follows the respective MP, and 0 otherwise. The correspondence matrix P is calculated by P = Í A i,j aij . …
Figure 7
Figure 7. Figure 7: CA first and second dimension: MP positions for each party are disproportionately spread out along the second dimension, and the most important MPs (e.g., the party chairs or the ones with the most followers) tend to be placed close together, no matter the party. D CHE…
Figure 8
Figure 8. Figure 8: Share of unretrieved articles per outlet. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_8.png]
Figure 9
Figure 9. Figure 9: Topic model evaluation scores. The preprocessing of the raw articles consists of stop-word and URL removal. Then, the articles are tokenized and lemmatized using the Python library spaCy, treating named entities and compound nouns as single tokens. The sentence “John D…
Figure 10
Figure 10. Figure 10: Histograms of max. topic probabilities µi (left) and max. metatopic probabilities µ˜i (right). Equivalently, we define µ˜i as the maximum of the metatopic probability distribution of topic i [PITH_FULL_IMAGE:figures/full_fig_p032_10.png]
Figure 11
Figure 11. Figure 11: Distribution of news by focus.de on Crime/Personal Security. 33 [PITH_FULL_IMAGE:figures/full_fig_p033_11.png]
Figure 12
Figure 12. Figure 12: Other topics attributed to metatopic ‘Environment and Climate Change’ 34 [PITH_FULL_IMAGE:figures/full_fig_p034_12.png]

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Reference graph

Works this paper leans on

54 extracted references · 52 canonical work pages

  1. [1]

    Altay, S., Berriche, M., and Acerbi, A. (2023). Misinformation on misinformation: Conceptual and methodological challenges. Social media+ society , 9(1):20563051221150412

    work page 2023

  2. [2]

    Aruguete, N., Calvo, E., and Ventura, T. (2021). News Sharing , Gatekeeping , and Polarization : A Study of the \# Bolsonaro Election . Digital Journalism , 9(1):1--23

    work page 2021

  3. [3]

    Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook . Science , 348(6239):1130--1132

    work page 2015

  4. [4]

    Barber \'a , P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using twitter data. Political analysis , 23(1):76--91

    work page 2015

  5. [5]

    Barber \'a , P. (2021). Estimating ideological positions with twitter data. https://github.com/pablobarbera/twitter_ideology

    work page 2021

  6. [6]

    T., Nagler, J., Tucker, J

    Barber \'a , P., Jost, J. T., Nagler, J., Tucker, J. A., and Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological science , 26(10):1531--1542

    work page 2015

  7. [7]

    and Messing, S

    Bond, R. and Messing, S. (2015). Quantifying social media’s political space: Estimating ideology from publicly revealed preferences on facebook. American Political Science Review , 109(1):62--78

    work page 2015

  8. [8]

    Bonica, A. (2014). Mapping the Ideological Marketplace . American Journal of Political Science , 58(2):367--386

    work page 2014

Show all 54 references
  1. [9]

    and Vaccari, C

    Chadwick, A. and Vaccari, C. (2019). News sharing on UK social media: Misinformation , disinformation, and correction. Publisher: Loughborough University

    2019

  2. [10]

    L., Zollo, F., Quattrociocchi, W., and Scala, A

    Cinelli, M., Brugnoli, E., Schmidt, A. L., Zollo, F., Quattrociocchi, W., and Scala, A. (2020). Selective exposure shapes the Facebook news diet. PloS one , 15(3):e0229129. Publisher: Public Library of Science San Francisco, CA USA

    2020

  3. [11]

    D., Gon c alves, B., Flammini, A., and Menczer, F

    Conover, M. D., Gon c alves, B., Flammini, A., and Menczer, F. (2012). Partisan asymmetries in online political activity. EPJ Data science , 1:1--19

    2012

  4. [12]

    M., Pinto, S., Serafino, M., Moss, F., Cicchini, T., Makse, H

    del Pozo, S. M., Pinto, S., Serafino, M., Moss, F., Cicchini, T., Makse, H. A., and Balenzuela, P. (2023). The hidden dimension of information diffusion: A latent space representation of Social Media News Sharing behavior. arXiv:2311.10837 [physics]

    2023 arXiv

  5. [13]

    A., and Nagler, J

    Eady, G., Bonneau, R., Tucker, J. A., and Nagler, J. (2020). News sharing on social media: Mapping the ideology of news media content, citizens, and politicians. Publisher: OSF Preprints

    2020

  6. [14]

    Eady, G., Nagler, J., Guess, A., Zilinsky, J., and Tucker, J. A. (2019). How many people live in political bubbles on social media? Evidence from linked survey and Twitter data. Sage Open , 9(1):2158244019832705. Publisher: SAGE Publications Sage CA: Los Angeles, CA

    2019

  7. [15]

    Gaisbauer, F., Pournaki, A., Banisch, S., and Olbrich, E. (2023). Grounding force-directed network layouts with latent space models. Journal of Computational Social Science , 6(2):707--739

    2023

  8. [16]

    Garimella, K., Smith, T., Weiss, R., and West, R. (2021). Political Polarization in Online News Consumption . Proceedings of the International AAAI Conference on Web and Social Media , 15:152--162

    2021

  9. [17]

    Green, J., McCabe, S., Shugars, S., Chwe, H., Horgan, L., Cao, S., and Lazer, D. (2025). Curation bubbles. American Political Science Review , pages 1--19

    2025

  10. [18]

    Guess, A., Aslett, K., Tucker, J., Bonneau, R., and Nagler, J. (2021a). Cracking open the news feed: Exploring what us Facebook users see and share with large-scale platform data. Journal of Quantitative Description: Digital Media , 1

    2021

  11. [19]

    Guess, A. M. (2021). ( Almost ) Everything in Moderation : New Evidence on Americans ' Online Media Diets . American Journal of Political Science , 65(4):1007--1022. Publisher: Wiley Online Library

    2021

  12. [20]

    M., Barberá, P., Munzert, S., and Yang, J

    Guess, A. M., Barberá, P., Munzert, S., and Yang, J. (2021b). The consequences of online partisan media. Proceedings of the National Academy of Sciences , 118(14):e2013464118. Publisher: National Acad Sciences

    2021

  13. [21]

    Himelboim, I., McCreery, S., and Smith, M. (2013). Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on twitter. Journal of computer-mediated communication , 18(2):154--174

    2013

  14. [22]

    D., Raftery, A

    Hoff, P. D., Raftery, A. E., and Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the american Statistical association , 97(460):1090--1098

    2002

  15. [23]

    Honnibal, M., Montani, I., Van Landeghem, S., and Boyd, A. (2020). spaCy: Industrial-strength Natural Language Processing in Python

    2020

  16. [24]

    Jolly, S., Bakker, R., Hooghe, L., Marks, G., Polk, J., Rovny, J., Steenbergen, M., and Vachudova, M. A. (2020). Ches 2019 codebook. (No Title)

    2020

  17. [25]

    Jolly, S., Bakker, R., Hooghe, L., Marks, G., Polk, J., Rovny, J., Steenbergen, M., and Vachudova, M. A. (2022). Chapel hill expert survey trend file, 1999--2019. Electoral studies , 75:102420

    2022

  18. [26]

    and Stark, B

    J \"u rgens, P. and Stark, B. (2022). Mapping exposure diversity: The divergent effects of algorithmic curation on news consumption. Journal of Communication , 72(3):322--344

    2022

  19. [27]

    o nig, T., Sch \

    K \"o nig, T., Sch \"u nemann, W. J., Brand, A., Freyberg, J., and Gertz, M. (2022). The epinetz twitter politicians dataset 2021. a new resource for the study of the german twittersphere and its application for the 2021 federal elections. Politische Vierteljahresschrift , 63(...

    2022

  20. [28]

    Kriesi, H., Grande, E., Lachat, R., Dolezal, M., Bornschier, S., and Frey, T. (2006). Globalization and the transformation of the national political space: Six european countries compared. European journal of political research , 45(6):921--956

    2006

  21. [29]

    S., Karnowski, V., and Keyling, T

    Kümpel, A. S., Karnowski, V., and Keyling, T. (2015). News Sharing in Social Media : A Review of Current Research on News Sharing Users , Content , and Networks . Social Media + Society , 1(2):2056305115610141. Number: 2 Publisher: SAGE Publications Ltd

    2015

  22. [30]

    Mau, S., Lux, T., and Westheuser, L. (2023). Triggerpunkte: Konsens und konflikt in der gegenwartsgesellschaft. (No Title)

    2023

  23. [31]

    McPherson, M., Smith-Lovin, L., and Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology , 27(1):415--444

    2001

  24. [32]

    Mukerjee, S., Majó-Vázquez, S., and González-Bailón, S. (2018). Networks of audience overlap in the consumption of digital news. Journal of Communication , 68(1):26--50. Publisher: Oxford University Press

    2018

  25. [33]

    T., and Nielsen, R

    Newman, N., Fletcher, R., Eddy, K., Robertson, C. T., and Nielsen, R. K. (2023). Reuters institute digital news report 2023

    2023

  26. [34]

    B., and Robinson, T

    Ohme, J., Araujo, T., Boeschoten, L., Freelon, D., Ram, N., Reeves, B. B., and Robinson, T. N. (2024). Digital trace data collection for social media effects research: Apis, data donation, and (screen) tracking. Communication Methods and Measures , 18(2):124--141

    2024

  27. [35]

    and Banisch, S

    Olbrich, E. and Banisch, S. (2021). The rise of populism and the reconfiguration of the german political space. Frontiers in big Data , 4:731349

    2021

  28. [36]

    B., Bechmann, A., and Petersen, M

    Osmundsen, M., Bor, A., Vahlstrup, P. B., Bechmann, A., and Petersen, M. B. (2021). Partisan polarization is the primary psychological motivation behind political fake news sharing on Twitter . American Political Science Review , 115(3):999--1015. Publisher: Cambridge University Press

    2021

  29. [37]

    Ou-Yang, L. (2020). newspaper3k. (No Title)

    2020

  30. [38]

    Post, S., Bienzeisler, N., and Pannach, F. (2023). An issue public’s confirmation-biased news feeding in changing political constellations: A quasi-experimental field study in the German conflict over genome editing. New Media & Society , page 14614448231185764. Publisher: SAG...

    2023

  31. [39]

    Quandt, T. (2008). (no) News on the World Wide Web ?: A comparative content analysis of online news in Europe and the United States . Journalism Studies , 9(5):717--738. Publisher: Routledge \_eprint: https://doi.org/10.1080/14616700802207664

    2008 doi

  32. [40]

    Ramaciotti Morales, P., Cointet, J.-P., Muñoz Zolotoochin, G., Fernández Peralta, A., Iñiguez, G., and Pournaki, A. (2022). Inferring attitudinal spaces in social networks. Social Network Analysis and Mining , 13(1):14

    2022

  33. [41]

    E., Stewart, B

    Roberts, M. E., Stewart, B. M., and Tingley, D. (2019). Stm: An r package for structural topic models. Journal of statistical software , 91:1--40

    2019

  34. [42]

    Rogers, R. (2018). Social media research after the fake news debacle. Partecipazione e Conflitto , 11(2):557--570

    2018

  35. [43]

    Schwaiger, L. (2022). Gegen die \"O ffentlichkeit: alternative Nachrichtenmedien im deutschsprachigen Raum . transcript Verlag

    2022

  36. [44]

    Strippel, C., Gong, B., Heft, A., Stolze, M., Kuznetsova, E., Ohme, J., Vziatysheva, V., and Gaisbauer, F. (2024). The diversity of alternative media users: Socio-demographics, political attitudes, and social media use. under review

    2024

  37. [45]

    Trilling, D. (2019). Conceptualizing and Measuring News Exposure as Network of Users and News Items . KölnHerbert von Halem Verlag

    2019

  38. [46]

    and Knudsen, E

    Trilling, D. and Knudsen, E. (2023). Drivers of News Sharing : How Context , Content , and User Features Shape Sharing Decisions on Facebook . Digital Journalism , 0(0):1--22. Publisher: Routledge \_eprint: https://doi.org/10.1080/21670811.2023.2255224

    2023 arXiv

  39. [47]

    Trilling, D., Tolochko, P., and Burscher, B. (2017). From Newsworthiness to Shareworthiness : How to Predict News Sharing Based on Article Characteristics . Journalism & Mass Communication Quarterly , 94(1):38--60. Publisher: SAGE Publications Inc

    2017

  40. [48]

    and Rieger, J

    von Nordheim, G. and Rieger, J. (2020). Im Zerrspiegel des Populismus . Publizistik , 65(3):403--424

    2020

  41. [49]

    Vosoughi, S., Roy, D., and Aral, S. (2018). The spread of true and false news online. science , 359(6380):1146--1151

    2018

  42. [50]

    E., Lane, D

    Weeks, B. E., Lane, D. S., Kim, D. H., Lee, S. S., and Kwak, N. (2017). Incidental Exposure , Selective Exposure , and Political Information Sharing : Integrating Online Exposure Patterns and Expression on Social Media . Journal of Computer-Mediated Communication , 22(6):363--379

    2017

  43. [51]

    Weischenberg, S., Malik, M., and Scholl, A. (2006). Journalismus in deutschland 2005. Media perspektiven , 7(2006):346--361

    2006

  44. [52]

    and Mendez, F

    Wheatley, J. and Mendez, F. (2021). Reconceptualizing dimensions of political competition in europe: A demand-side approach. British Journal of Political Science , 51(1):40--59

    2021

  45. [53]

    Wischnewski, M., Bruns, A., and Keller, T. (2021). Shareworthiness and motivated reasoning in hyper-partisan news sharing behavior on Twitter . Digital Journalism , 9(5):549--570. Number: 5 Publisher: Taylor & Francis

    2021

  46. [54]

    M., and Weeks, B

    Wojcieszak, M., de Leeuw, S., Menchen-Trevino, E., Lee, S., Huang-Isherwood, K. M., and Weeks, B. (2023). No polarization from partisan news: Over -time evidence from trace data. The International Journal of Press/Politics , 28(3):601--626. Publisher: SAGE Publications Sage CA...

    2023

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.