Cross-Partisan Interactions (CPIs)

• Cross-Partisan Interactions (CPIs) are defined as exchanges between politically opposing groups, operationalized through network ties, directed replies, and structured conversational experiments.
• The literature employs diverse methodologies—from dynamic network models and Bayesian learning to platform-specific studies on TikTok, YouTube, Twitter, and Reddit—to quantify and analyze CPIs.
• Empirical evidence shows that CPIs are asymmetrically distributed, vary in toxicity and visibility, and influence integration and polarization in nuanced, context-specific ways.

Searching arXiv for the listed CPI-related papers to ground the article in current preprints and provided sources. Cross-Partisan Interactions (CPIs) are interactions across party lines between actors with opposing political leanings. In the cited literature, the construct has no single universal variable; instead, it is operationalized as out-group ties in weighted or signed networks, replies and mentions between opposing users, duet responses across partisan camps, cross-ideological commenting, post-exposure sharing of out-party media, and structured conversational encounters in which partisan identity and stance are manipulated independently (Evans et al., 2018, Çetinkaya et al., 12 Apr 2025, Kim et al., 14 Jun 2026). Across these formulations, the central analytical problem is not only whether CPIs exist, but how they persist, how they are distributed asymmetrically across sides, how platform affordances alter their visibility and tone, and whether they produce integration, hostility, or only short-lived reductions in polarization (Wu et al., 2021, Xia et al., 2024, Biswas et al., 20 Mar 2026).

1. Conceptual scope and operational definitions

In dynamic-network work, CPIs appear as cross-group ties between nodes whose political beliefs have opposite signs. In "Opinion formation on dynamic networks: identifying conditions for the emergence of partisan echo chambers" (Evans et al., 2018), CPI-like ties are the weighted links $C_{ij}$ between agents with opposite-sign opinions $X_i$ and $X_j$. They are not stored in a separate variable; they are the out-group edges of a signed-opinion network, and their strength evolves jointly with opinions. In that formulation, a cross-partisan tie is costly when $X_iX_j<0$, and beneficial when the general value of connection outweighs the disagreement penalty.

On social platforms, the literature usually defines CPIs through directed communicative acts. On TikTok, a CPI is a duet from one partisan camp to the other, with directed links $R \rightarrow D$ and $D \rightarrow R$ distinguished from $R \rightarrow R$ and $D \rightarrow D$ (Serrano et al., 2020). On YouTube, a cross-partisan comment is a comment by a user whose inferred leaning differs from the leaning of the video or channel, while reply-level cross-partisanship is defined relative to the ideology of the replied-to user or thread (Wu et al., 2021). On Twitter, "Cross-Partisan Interactions on Twitter" (Çetinkaya et al., 12 Apr 2025) restricts the definition to direct replies between users of different political orientations. On Reddit, CPIs are comment-reply interactions between users with opposite inferred leanings in a politically mixed non-partisan community (Xia et al., 2024). In elite-audience settings, cross-cutting interactions are retweets, replies, or mentions by legislators directed at users whose inferred leaning is opposite the legislator’s own (Biswas et al., 20 Mar 2026).

The concept is broadened further in AI and LLM studies. In "Challenging Partisan Expectations Reduces Political Polarization" (Kim et al., 14 Jun 2026), the crucial CPI is a structured conversation in a $2\times 2$ design where the chatbot’s presented partisan identity and issue stance are crossed as Ingroup Agree, Ingroup Disagree, Outgroup Agree, and Outgroup Disagree. In "Synthetic Contact with AI Reduces Cross-Partisan Animosity" (Lira et al., 2 Jul 2026), CPIs are outgroup conversations with a chatbot prompted to represent the political outgroup. In "Can AI Debias the News? LLM Interventions Improve Cross-Partisan Receptivity but LLMs Overestimate Their Own Effectiveness" (Feroz et al., 1 May 2026), the interaction is operationalized as trust-relevant judgments of partisan news headlines by ideological outgroup readers. In "Beyond Western Politics: Cross-Cultural Benchmarks for Evaluating Partisan Associations in LLMs" (Kumar et al., 24 Sep 2025), the relevant interaction is a pairwise plausibility judgment in which a model must decide which of two near-identical partisan statements is more logically plausible.

2. Formal models of persistence, segregation, and learning

The most explicit dynamical account treats CPIs as endogenous ties whose persistence depends on a payoff tradeoff. In (Evans et al., 2018), each node’s fitness is

$$f_{i}(X,C) = \alpha|X_{i}|+\beta\sum_{i \neq j}C_{i,j}X_{i}X_{j} + \gamma\sum_{i \neq j} C_{i,j},$$

and the total network fitness is

$X_i$0

Here, $X_i$1 rewards strong opinions, $X_i$2 rewards agreement with neighbors and penalizes disagreement, and $X_i$3 rewards maintaining connections in general. Opinions and ties are both updated by small random mutations followed by local accept/reject steps based on fitness improvement. The tie-level mechanism is summarized by

$X_i$4

For opposite-sign nodes, $X_i$5, so the disagreement term can overcome the connection benefit. If $X_i$6, the cross-partisan tie is disadvantageous and tends to weaken.

This produces a threshold result. When $X_i$7, nodes with opposite views are much more likely to become disconnected, and the network tends to divide into two echo chambers with splitting views; when $X_i$8, cohesive communities with consensus can emerge (Evans et al., 2018). The division metric is $X_i$9, where $X_j$0 is the total weight of within-group ties and $X_j$1 the total weight of between-group ties. High values therefore correspond to weak or rare CPIs, while low or intermediate values correspond to more substantial cross-group contact. The same model states that a node tends to increase opinion magnitude when

$X_j$2

so opinion extremity and cross-party tie collapse can reinforce one another.

A second formal tradition models CPIs through signed Bayesian learning with allies, opponents, and fixed partisans. In (Bu et al., 2023), agents first update on common evidence and then undergo peer-pressure adjustment according to a signed adjacency matrix $X_j$3, where $X_j$4 denotes allies and $X_j$5 denotes opponents. Obdurate partisans have fixed belief densities and influence others without updating themselves. The qualitative results are sharply topology-dependent. In allies-only networks, even one partisan stops asymptotic learning and induces turbulent nonconvergence. In opponents-only networks, asymptotic learning occurs whether or not partisans are present. In mixed networks, outcomes depend sensitively on where partisans reside; strongly balanced triads can display asymptotic learning without a partisan and intermittency with one. A plausible implication is that CPI outcomes depend not only on whether interaction is cross-partisan, but on whether the surrounding structure is conformist, antagonistic, or mixed.

3. Measurement infrastructures across platforms

Platform studies differ most in how they infer leaning and delimit interaction events. On TikTok, (Serrano et al., 2020) begins from #republican and #democrat hashtag searches crawled on February 1, 2020, yielding 3,310 initial videos and then 7,825 videos after sound-based duet expansion. Every original video and duet was manually labeled as pro-Republican, pro-Democrat, or nonpartisan, with two authors coding independently and disagreements resolved by a third author. The final partisan sample contained 5,946 partisan videos, of which 3,987 were Republican and 1,959 Democratic, posted by 1,957 Republican users and 1,249 Democratic users. The study constructs a directed interaction graph with users as nodes and duet interactions as edges, and also treats TikTok political communication as a tree structure: level 1 is the political issue, level 2 original partisan videos, level 3 duets responding to originals, and level 4+ duets responding to duets.

On YouTube, (Wu et al., 2021) uses a much larger infrastructure: 274,241 political videos from 973 US partisan media channels, 133,810,991 comments, and 9,304,653 users between 2020-01-01 and 2020-08-31. Channel ideology is inherited from Media Bias/Fact Check mappings of associated websites, with center sources discarded. User ideology is inferred by first constructing seed users from hashtags, URLs, and subscriptions, and then training a Hierarchical Attention Network on comment histories. The final labeled population covers 6.53M users and 123M comments, or 91.9% of all comments. Replies and root comments are analyzed separately, which is crucial because cross-partisan toxicity behaves differently in those two regimes.

Twitter/X studies use several inference pipelines. In (Çetinkaya et al., 12 Apr 2025), political orientation is estimated by the Barberá et al. following-based method on a $X_j$6 scale, with $X_j$7 treated as neutral and excluded. The final dataset contains 2,561,846 tweets from 726,140 users, including 1,594,271 replies and 967,575 root tweets. In (Singh et al., 10 Apr 2026), political alignment of original posts and reply authors is inferred with GPT-4o-mini, validated against 384 human-coded posts and 384 replies; reply-author labels are then aggregated to the user level by majority vote. The analytic corpus contains 261,501 original tweets and 2,415,781 direct replies. On Reddit, (Xia et al., 2024) infers user leaning from prior activity in partisan subreddits using

$X_j$8

and

$X_j$9

with subreddit leanings binned into $X_iX_j<0$0. The focal arena is r/news during August–September of each year from 2014 to 2018, with 30-day before and after windows around a user’s first received reply or first comment.

Elite-audience CPI studies use follower-based inference rather than content-only inference. In (Biswas et al., 20 Mar 2026), a follower’s attention leaning is

$X_iX_j<0$1

where $X_iX_j<0$2 and $X_iX_j<0$3 are the numbers of Democratic and Republican legislators followed. Manually calibrated thresholds $X_iX_j<0$4 and $X_iX_j<0$5 identify Democrat-leaning and Republican-leaning audiences. The main sample contains about 4 million tweets from 3,568 legislators in 2020–2021, with about 1.17 million posts having an inferable interaction partner and around 82,000 containing at least one cross-cutting interaction.

4. Empirical regularities: asymmetry, toxicity, and visibility

A recurrent finding is that CPIs are substantial but asymmetric. On TikTok, Democratic users directed 81% of their duets to Republicans, whereas Republican users directed 22.6% of their duets to Democrats; the observed-to-expected ratios were 1.35 for Democrat $X_iX_j<0$6 Republican and 0.57 for Republican $X_iX_j<0$7 Democrat (Serrano et al., 2020). On YouTube, among users with at least 10 comments, 82.3% of conservatives and 62.2% of liberals commented at least once on opposite-leaning videos; the median cross-partisan share of comments was 22.2% for conservatives and 4.8% for liberals (Wu et al., 2021). On Twitter, 661,661 replies, or 34% of analyzed replies, were classified as CPIs; within-party rates were close, at 34.3% of Democrat replies and 33.2% of Republican replies, but the stance profile differed, with Democrats’ cross-partisan replies more accusatory, dismissive, hostile, and critical than their intra-party replies, while Republicans showed less difference between in-party and cross-party tone (Çetinkaya et al., 12 Apr 2025).

The literature also separates prevalence from qualitative tone. On YouTube, direct comments on video content show only small ideology differences in toxicity, but replies are markedly different: cross-partisan replies are much more toxic than co-partisan replies on both left-leaning and right-leaning videos, and they are especially toxic on the replier’s home turf (Wu et al., 2021). On X during the 2024 election, Republican-leaning original posts had median toxicity 0.144 and Democratic-leaning posts 0.129, yet replies to Democratic posts had median toxicity 0.199 compared with 0.178 for replies to Republican posts (Singh et al., 10 Apr 2026). The explanatory variable was not merely per-reply hostility but reply composition: Democratic posts received 1,145,347 total replies, including 756,671 Republican replies, while Republican posts received 708,705 total replies, including 205,607 Democratic replies. The paper therefore attributes the asymmetry primarily to the much larger volume of Republican cross-partisan replies to Democratic posts.

Platform affordances alter both visibility and interpretation. YouTube’s Top comments ranking modestly reduces exposure to ideological opposition: on left-leaning videos, conservatives made up 26.3% of all comments but only 20.5% of the first-position comments and 22.8% of the twentieth-position comments (Wu et al., 2021). TikTok’s duet format makes cross-partisan exchange more visibly performative, because responses preserve original audio and add facial expressions, text overlays, images, or video performance; the study therefore characterizes the platform as more like public debate than comment-thread interaction (Serrano et al., 2020). These findings undercut a simple equation of CPI with deliberation. Several studies state directly that cross-partisan interaction may be sarcastic, mocking, ridiculing, or otherwise hostile rather than integrative (Serrano et al., 2020, Çetinkaya et al., 12 Apr 2025).

5. Behavioral consequences, integration, and feedback effects

The strongest test of whether CPIs actually integrate behavior comes from Reddit. In (Xia et al., 2024), receiving a cross-party reply in r/news is not significantly associated with increased subsequent out-party subreddit activity unless the original comment is already a nested reply. By contrast, receiving a cross-party reply is significantly associated with increased in-party activity in several years, but the effect is comparable to that of receiving a same-party reply. The paper therefore argues against both a strong automatic depolarization story and a strong backfire story, and instead interprets the result as a highly conditional depolarization effect that is likely part of a more general dynamic of feedback-boosted engagement.

An elite-audience version of this feedback logic appears in (Biswas et al., 20 Mar 2026). Engagement is normalized by an overperforming score relative to a legislator’s own recent baseline, with $X_iX_j<0$8 and a 14-day window. Baseline effects are asymmetric: Republicans receive lower engagement when replying to or mentioning Democrats, while Democrats receive modest engagement gains when replying to or mentioning Republicans. Yet the later-stage feedback pattern is more constructive than the baseline might suggest. High-engagement CPIs increase future cross-party mentions by 2.2% for Republicans and 1.3% for Democrats, and are associated with later increases in hedging, subjective language, causal reasoning, positive emotion, and topical references in subsequent CPIs. The paper explicitly notes that it does not find reinforcement of anger, anxiety, or toxicity in future CPIs, even though such features can sometimes attract short-run engagement.

A different behavioral consequence appears in work on partisan sharing. "Partisan Sharing: Facebook Evidence and Societal Consequences" (An et al., 2014) shifts the focus from exposure to post-exposure action, arguing that users may be exposed to politically diverse articles but share mainly like-minded ones. In that operationalization, partisan sharing functions as a CPI-like post-exposure behavior. Among self-identified partisans sharing political news, liberals shared roughly one counter-attitudinal article every 6 like-minded articles, while conservatives shared one counter-attitudinal article every 4 like-minded articles. Partisan skew was lowest during the election month and increased afterward, and partisan sharing was associated both with distorted perceptions of outlet bias and with higher political knowledge and likelihood of voting. This body of work suggests that cross-partisan exposure, cross-partisan reply, and cross-partisan transmission should not be treated as interchangeable.

6. Interventions, synthetic contact, and LLM-mediated partisan interaction

Intervention studies recast CPIs as intentionally structured contact. In (Kim et al., 14 Jun 2026), a preregistered experiment with $X_iX_j<0$9 U.S. adults used a chatbot whose partisan identity and issue stance were independently manipulated. Relative to Ingroup Agree, Ingroup Disagree reduced affective polarization by $R \rightarrow D$0 and Outgroup Agree reduced it by $R \rightarrow D$1; Outgroup Agree also reduced perceived issue polarization by $R \rightarrow D$2. The core mechanism is expectation violation: disagreeing ingroup members and agreeing outgroup members reduce polarization more than expectation-confirming conditions. The effects did not meaningfully change participants’ own policy positions, most effects disappeared over one month, and the same interactions were experienced as less satisfying, even though objective discussion-quality measures did not decline.

"Synthetic Contact with AI Reduces Cross-Partisan Animosity" (Lira et al., 2 Jul 2026) asks whether AI can serve as a substitute for human CPI. Across five preregistered studies totaling $R \rightarrow D$3, the barrier to entry was lower for AI than for humans: a three-minute conversation with a human outgroup partner was traded off against 9.65 minutes of contemplating one’s own death, compared with 5.06 minutes for an AI outgroup partner. A single ten-minute outgroup-bot conversation improved belief accuracy about the other side by 0.39 points on a 5-point environmental-attitudes scale and increased outgroup warmth by 4.3 thermometer points. In a behavioral choice study, participants who first spoke with an outgroup bot were more likely to choose a real cross-partisan conversation afterward, with $R \rightarrow D$4. The one-week warmth effect mostly decayed, though pooled follow-up analyses showed $R \rightarrow D$5, rising to $R \rightarrow D$6 among the more extreme half. The content audit found that outgroup bots differed from controls more in stereotype-disconfirming substance than in empathy or friendliness, so the paper interprets the mechanism primarily as cognitive correction rather than an affective warmth route.

LLM research extends CPIs from contact to media receptivity and latent representational structure. In (Feroz et al., 1 May 2026), subtle lexical debiasing of liberal MSNBC headlines had no effect on conservatives’ trustworthiness, comprehensiveness, or openness judgments, whereas substantive reframing significantly increased conservatives’ perceived trustworthiness, perceived completeness, and willingness to engage, without producing a backfire effect among liberals. The same paper finds that silicon participants generated by an LLM strongly overestimated the effectiveness of the shallow lexical intervention and often exaggerated the size of the reframing effect. In (Kumar et al., 24 Sep 2025), pairwise plausibility judgments reveal asymmetrical partisan associations rather than conversational effects: across six frontier models, overall bias susceptibility ranged from 91.6% to 100%; in U.S. party-level results, Democrats received 600 positive associations to Republicans’ 48, while Republicans received 580 negative associations to Democrats’ 43; Democratic leaders had a 93.0% positive-bias rate and Republican leaders 6.2%. The paper also reports that neutral prompts can produce higher bias than adversarial prompts. These studies relocate CPI analysis into model behavior itself, where the relevant question is no longer whether opposing partisans talk, but how systems compare, frame, and differentially legitimate them.

Taken together, the literature depicts CPIs as neither rare nor uniformly salutary. They can collapse when disagreement penalties exceed the value of connection, remain visible but toxic under reply-heavy and performative architectures, stimulate only conditional movement toward out-party engagement, or briefly reduce affective polarization when expectations are violated or misperceptions are corrected (Evans et al., 2018, Wu et al., 2021, Xia et al., 2024, Kim et al., 14 Jun 2026). A consistent theme is that prevalence, symmetry, visibility, tone, and downstream behavioral effect are analytically distinct properties. CPIs therefore function less as a single indicator of democratic health than as a family of cross-boundary processes whose consequences depend on topology, platform design, rhetorical style, and the incentives attached to remaining connected despite disagreement.