The context matters enormously. Arab society constitutes roughly 21 percent of Israel’s population and represents the country’s largest ethnic minority. Despite high rates of internet and social media use among Arab women in Israel, survey data indicate that only about 39.8 percent of women feel safe sharing personal information online, and approximately half refrain from posting photos at all due to insecurity rooted in social control and family surveillance. Divorce, in particular, remains heavily stigmatized within a traditional, family-centered social structure, even as rising education and economic independence have made leaving a marriage a more viable option for some women. Divorce rates within Arab society in Israel have been increasing, especially among Muslim families, and women who separate can face social exclusion, monitoring, and even violence. Against this backdrop, the digital sphere becomes what the researchers describe as an ambivalent arena—one that simultaneously reproduces power hierarchies and enables solidarity, support, and identity negotiation.

To capture this tension empirically, the researchers conducted 20 semi-structured in-depth interviews with divorced Arab women aged 29 to 45, all of whom held academic degrees and actively shared personal content online. Sixteen participants were Muslim, two Christian, and two Druze; most lived in traditional Arab towns and villages in northern and central Israel. Crucially, the study added a methodological twist: each participant was invited to select a meaningful social media post related to her divorce narrative, and these posts served as elicitation materials during the interviews, anchoring abstract reflections in concrete digital artifacts. Interviews were conducted in Arabic, transcribed verbatim, and analyzed using a predominantly inductive, semantic thematic analysis following Braun and Clarke’s six-phase approach. The first author, a native Arabic speaker with deep familiarity with the community, translated and back-translated quotations to preserve conceptual accuracy, while the second author provided an external analytic perspective. The team presented de-identified, text-only reconstructions of selected posts rather than original screenshots, an extra safeguard to prevent reverse identification of participants.

The analysis yielded four interrelated themes: online self-disclosure, online support, motivations for sharing, and online challenges. Perhaps the most striking finding concerns how disclosure operates not as a binary between openness and concealment but as what the authors call “negotiated visibility”—a continuous process of deciding what aspects of the self become visible, to whom, and under what conditions. Some women made bold public declarations. One participant updated her Facebook relationship status from married to divorced shortly after separating, noting that she “did not hesitate to pay the price.” Another posted a personal photo declaring her divorce and opened herself to anonymous questions. Others chose far more circuitous routes, revealing how finely calibrated these decisions can be.

Visual content emerged as especially fraught. One participant described posting pictures of herself engaging in an activity that symbolized a new chapter after her divorce, only to receive harsh criticism, including from people close to her. Her solution was telling: she removed the photo from her permanent feed but continued to circulate it through her ephemeral story. This pattern—using temporary content for sensitive emotions and permanent posts for safer material—recurred across interviews. Participants also described professional disclosure as a more socially legitimate channel for visibility, with one woman marketing workshops for divorced mothers and using her own story as an Arab Muslim woman who divorced within a traditional society to raise awareness and encourage open dialogue. Platform choice itself became a strategic variable: Instagram was preferred over Facebook for its greater privacy, better control over commenters, and fewer male users.

Indirect disclosure and impression management formed another sophisticated layer of the negotiation. One participant explained that she could not publicly share that her divorce followed domestic violence, fearing she would be perceived as weak. Instead, she responded supportively to posts written by other women describing abuse, knowing readers might infer her own history but finding that far less exposing than a direct post. Others projected strength while concealing vulnerability—one woman admitted she presented herself as strong and independent online while secretly aspiring to a new relationship, too afraid of criticism to voice that desire anywhere. Yet the study also documented moments of radical authenticity. One participant posted about pursuing a second relationship after divorce as a mother in her forties, directly confronting a taboo her own parents could not understand. These accounts echo Goffman’s classic theory of self-presentation and Communication Privacy Management theory, which conceptualizes disclosure as the regulation of boundaries around private information—processes complicated online by persistent content, imagined audiences, and context collapse.

Beyond self-expression, social media functioned as a genuine support infrastructure. Participants described closed groups for divorced women where they exchanged emotional encouragement during moments of acute loneliness, professional advice from therapists, and practical information about child support, legal rights, and benefits. One participant credited another divorced woman’s courageous online narrative with giving her the strength to move out and rent her own home despite her father’s threats of family ostracism. Importantly, support flowed in both directions: women were not merely recipients but active providers of empathy, guidance, and lived experience, reinforcing previous findings that online support develops through reciprocal exchange. One participant founded a large closed group for divorced women and single mothers precisely because, in her words, they share similar lives, pains, and challenges and can support one another, potentially even organizing activities and trips with their children.

The motivations behind this risky sharing were distinctly social. Many participants framed their disclosure as deliberate activism aimed at changing attitudes toward divorce. One woman declared that she shares her experiences “to convey a message to women and men in my society, that it is possible to divorce peacefully and quietly, by mutual agreement, and without wars or defamation.” Another posted family photographs, including images with her ex-husband’s child, to demonstrate that amicable divorce is possible and to create new discourse around family life. These efforts extended to identity reconstruction: participants emphasized professional achievements, motherhood, personal growth, and alternative family narratives rather than allowing divorce to become their defining characteristic. One participant described the most meaningful post of her life as a photo from her daughter’s graduation, chosen because it publicly acknowledged her maternal role and both sides of the family.

The risks, however, were neither hypothetical nor peripheral. Participants reported online harassment from men who appeared to view divorced women and single mothers as “easy prey,” indirect threats following media exposure, and invasive surveillance by family members—one woman’s husband had hacked her computer and accounts during the marriage, permanently reshaping her post-divorce disclosure habits. A single screenshot, as one participant noted, can be used against a woman indefinitely. Others described asymmetrical visibility, where anonymous strangers probed their personal lives while they remained exposed. Criticism could be morally corrosive rather than merely disagreeable: one participant who publicly announced her divorce described feeling pride and liberation, only to have a judgmental response shake her so deeply that she no longer wanted to share. In response, women deployed stigma-management strategies ranging from toggling their relationship status on and off to leaving it blank entirely—one participant observed that in society’s eyes, the label “divorced” cancels all of a woman’s achievements and identity.

The authors are careful about the study’s limits. The sample consisted entirely of highly educated women who had already shared divorce-related content online, so the experiences of women who choose silence or digital absence remain unexamined. Data were also collected between February and July 2025, during a period of ongoing war in Israel, which may have amplified perceptions of insecurity. Still, the findings carry significant theoretical weight. They demonstrate that online self-disclosure among culturally marginalized women is a socially situated practice negotiated across competing normative frameworks—communal patriarchal expectations on one side, a more socially liberal majority context on the other. Visibility operates simultaneously as a resource for support and identity reconstruction and as a source of stigma, surveillance, and risk. Rather than withdrawing from digital spaces, these women adapted, selecting different platforms, audiences, formats, and levels of exposure. In doing so, they transformed shared vulnerability into collective belonging and used the networked public square to contest dominant narratives about divorce, motherhood, and what a woman’s life after marriage can look like. The study ultimately positions online spaces as gendered environments where identity, legitimacy, and selfhood are continuously negotiated under culturally constrained conditions—a finding with implications far beyond the communities it documents.

Subject of Research: Online self-disclosure and stigma management among divorced Arab women in Israel

Article Title: Claiming voice in virtual spaces: identity, stigma, and support among divorced Arab women in Israel

Article References: Asley, T., & Gazit, T. (2026). Claiming voice in virtual spaces: identity, stigma, and support among divorced Arab women in Israel. Current Psychology, 45(18), Article 1527. https://doi.org/10.1007/s12144-026-10095-8

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10095-8

Keywords: social media, self-disclosure, divorce, Arab women, Israel, stigma, identity reconstruction, online support, gender, surveillance, qualitative research, digital spaces

The research team, led by Hong Wu, Quzhi Liu, and colleagues at Hohai University in Nanjing, designed a cross-sectional online survey around two parallel 11-item experience scales. Each item probed a distinct dimension of perceived support quality: acceptance, understanding, goal alignment, collaboration, professional competence, response efficiency, safety and privacy, trust, reduced concern about being judged, willingness for deep self-disclosure, and disclosure of negative emotions. Because every participant rated both modalities on identical items, the design enabled paired statistical comparisons that control for the stable personality traits and life circumstances each individual brings to any evaluation. This within-person approach matters enormously in this field, because people who choose to use chatbots may differ systematically from people who seek therapy, and those pre-existing differences can masquerade as modality effects in cruder between-group studies.

The statistical machinery behind the study was deliberately layered. The primary analysis used a paired-samples t test on a 10-item composite score, constructed after the researchers discovered that the response-efficiency item was not strictly measurement-equivalent across the two modalities, meaning that a chatbot’s instant reply and a therapist’s considered response may simply not measure the same underlying quality. To guard against distortion from this problematic item, the team removed it from both modality scores and re-ran the comparison. They also applied paired Wilcoxon signed-rank tests with Monte Carlo two-tailed p values and Holm correction to the item-level data, a procedure that controls the inflated risk of false positives when many comparisons are tested simultaneously. Finally, a repeated-measures linear mixed-effects model served as a sensitivity analysis, explicitly modeling the within-person paired structure and testing whether the size of the modality difference varied according to individual characteristics such as attachment style.

The headline finding was a small but statistically robust overall advantage for AI-based support. On the 10-item composite, AI support scored a mean of 57.77 with a standard deviation of 6.55, while human counseling scored 56.80 with a standard deviation of 6.75. The mean difference, calculated as human minus AI, was −0.97 points, with a 95 percent confidence interval spanning −1.73 to −0.21, a paired t value of −2.53 on 232 degrees of freedom, and a p value of .012. The standardized effect size was modest, dz = 0.17, which the researchers emphasize is a small difference. Yet its persistence after the potentially non-equivalent response-efficiency item was excluded suggests the pattern is not merely an artifact of comparing a chatbot’s instant replies with a therapist’s slower turnaround. At the item level, AI support received higher ratings on five exploratory indicators, while human counseling won on exactly two: mutual understanding and professional competence.

That two-item advantage for human counselors is arguably the most psychologically meaningful part of the results. Mutual understanding and professional competence are precisely the qualities that define the therapeutic relationship in classical accounts of counseling, and the study suggests they remain the territory where human practitioners are perceived to excel, even among people who also use and generally rate AI support favorably. Conversely, the domains where AI scored higher map onto what the researchers describe as low-threshold and low-social-threat experiences. Talking to a chatbot appears to reduce the fear of being judged, makes deep self-disclosure feel safer, and lowers the barrier to seeking support in the first place. For people who find the prospect of sitting across from a professional intimidating, a machine that never sighs, never frowns, and never files a clinical note about your worst thoughts may feel like a genuinely safer opening move.

Attachment theory provided the study’s central individual-difference framework, and the regression results revealed a coherent pattern. Attachment anxiety, characterized by a heightened need for reassurance and fear of rejection, positively predicted perceived experience quality in both modalities, meaning anxiously attached participants rated both AI support and human counseling more favorably. Attachment avoidance, characterized by discomfort with closeness and interdependence, negatively predicted both outcomes, so avoidantly attached participants rated both forms of support less favorably. In other words, the same relational dispositions color how people experience support whether it comes from a person or a machine, suggesting that chatbots do not escape the attachment dynamics that shape human relationships. Notably, specialized mental health and therapy chatbot use was associated with higher AI-experience scores, while associations involving shorter AI-use duration were less consistent, hinting that purpose-built tools may deliver a better experience than general-purpose assistants pressed into therapeutic duty.

The mixed-effects sensitivity analysis added a subtler layer to the attachment findings. The interaction between modality and attachment avoidance was statistically significant, indicating that the relative gap between AI-based support and human counseling was not uniform across participants but varied according to how avoidant they were in close relationships. Although the study’s published summary does not detail the direction of this interaction, its existence implies that attachment avoidance may shape which modality feels more comfortable, a possibility with direct clinical implications for matching people to support formats. Meanwhile, the hierarchical regression on the human-minus-AI difference score showed improved model fit after the attachment variables were added, but the final full model was not significant at the omnibus level, so the researchers treat the difference-score analysis as suggestive rather than conclusive.

The authors are unusually explicit about the limits of their evidence, and these caveats deserve as much attention as the findings. The design was cross-sectional and entirely self-report, so the data capture perceived experiences rather than objectively measured therapeutic outcomes, and no causal claims about which modality actually helps more can be sustained. The distributions showed restricted upper-end variability, with medians generally high and often identical across modalities, which compresses the statistical room to detect differences. The AI tools participants reported using were heterogeneous, spanning whatever chatbots they happened to try, and the 11-item experience indicators were newly developed for this study and remain exploratory rather than validated instruments. The researchers also caution that the sensitivity analysis relied on a composite score derived from ordinal item ratings, a measurement choice that warrants care when interpreting the small mean difference that survived.

What the study ultimately delivers is a differentiated map rather than a verdict. Among dual users, perceived experiences differed by domain rather than showing a uniform preference for either machines or humans. AI-based psychological support appears to excel as a low-barrier, judgment-free channel that encourages disclosure, while human counseling retains perceived advantages in mutual understanding and professional competence, the relational core of formal therapy. For clinicians, policymakers, and the rapidly growing industry building mental health chatbots, the implication is that the two modalities may be complements rather than competitors: AI could serve as an accessible first step or an adjunct between sessions, while human professionals remain the reference point for depth and expertise. As conversational AI becomes more capable and more widely adopted, studies that compare experiences within the same individuals, as this one does, will be essential for understanding not just whether people use these tools, but how the tools make them feel, and for whom they work best.

Subject of Research: Perceived experiences of AI-based psychological support compared with human counseling among adults who have used both modalities

Article Title: Differences in perceived experiences of AI-based versus human psychological support among dual users: paired comparisons, hierarchical regression, and repeated-measures sensitivity analysis

Article References: Wu, H., Liu, Q., Shi, Y., Yang, M., Zhang, J., & Zhang, Y. (2026). Differences in perceived experiences of AI-based versus human psychological support among dual users: paired comparisons, hierarchical regression, and repeated-measures sensitivity analysis. BMC Psychology. https://doi.org/10.1186/s40359-026-05676-y

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05676-y

Keywords: conversational AI, mental health chatbots, human counseling, attachment theory, self-disclosure, user experience, paired comparison, hierarchical regression, mixed-effects model, psychological support, trust, BMC Psychology

The study drew on data from 319 participants aged 65 and older who were enrolled in a community-based health screening program in Japan. This population matters because the overwhelming majority of older people worldwide live at home rather than in institutional care, and their day-to-day well-being hinges on informal networks of relatives, neighbors, and friends. To capture the dynamics of these networks, the team used two validated instruments. Social support was assessed with the Social Support Giving and Receiving Scale for the Elderly at Home, which measures support as a bidirectional phenomenon, acknowledging that older adults are not merely passive recipients of care but active contributors to the people around them. Self-disclosure was evaluated with the Self-Disclosure Questionnaire, a self-assessment tool developed specifically for research with community-dwelling older adults in Japan.

Rather than treating social support as a single continuous score, the researchers employed cluster analysis, a statistical technique that groups individuals according to shared patterns across multiple variables. This approach revealed three distinct profiles of social support among the participants. The first cluster was characterized by low receiving combined with high giving—older adults who actively helped others but received comparatively little in return. The second cluster showed both low receiving and low giving, representing people whose support relationships had thinned out in both directions. The third and largest-profile category comprised participants with balanced or high receiving, whose networks delivered support that met or exceeded what they provided. The existence of these distinct patterns underscores that social support in later life is not a uniform resource but a set of different relational configurations with potentially different consequences for health.

The demographic and functional differences between these clusters proved telling. Participants in the giving-oriented cluster—younger within the study range and marked by higher functional capability and more extensive social networks—were, in effect, the helpers of their communities. Their capacity to provide support reflected physical and social resources that many of their peers no longer possessed. By contrast, individuals in the receiving-oriented cluster tended to be older, with lower functional capability, consistent with the intuitive picture that as physical capacity declines, the balance of support shifts from giving toward receiving. This gradient illustrates a fundamental asymmetry in aging: the same person may move across these clusters over time as health and social circumstances change, and the transition can be abrupt when illness or functional loss strikes.

The centerpiece of the analysis, however, was the relationship between self-disclosure and social support. Using regression models that adjusted for relevant covariates, the researchers found that greater self-disclosure to family members was significantly associated with higher levels of both giving and receiving social support, with the association reaching a p-value below .001—a threshold indicating an extremely low probability that the finding arose by chance. In practical terms, older adults who opened up to their families about their inner lives were embedded in richer, more reciprocal webs of mutual assistance. The finding aligns with long-standing social psychological theory, from Altman and Taylor’s social penetration model, which describes how relationships deepen through progressively more personal disclosure, to exchange-based accounts showing that disclosure tends to be reciprocated, drawing conversation partners into closer, more supportive bonds.

Intriguingly, the pattern changed when the researchers examined disclosure to friends and acquaintances. Here, the association was confined to giving support, reaching statistical significance at p = .023. Older adults who shared more of themselves with friends and broader acquaintances were more likely to give help to others, but this outward-facing openness did not translate into greater receipt of support in return. One interpretation is that friendships in later life, while valuable and often rich in emotional rewards, are structurally different from family bonds. Friends may be similarly aged and similarly capable, and therefore less positioned—or less obligated—to deliver instrumental care when needs arise. Families, by contrast, typically span generations and carry normative expectations of caregiving, so that disclosure within them activates both emotional closeness and practical assistance.

The study’s implications extend to public health and community care policy, particularly in rapidly aging societies such as Japan, which has built a community-based integrated care system premised on informal support networks surrounding each older resident. If self-disclosure within families functions as a gateway to reciprocal support, then interventions that encourage open communication—family conversations, counseling approaches, or even structured programs that teach and normalize the sharing of personal concerns—could strengthen the informal safety net that formal services increasingly depend upon. The finding that family disclosure predicts both directions of support also suggests a possible lever for addressing the low-receiving, low-giving cluster, the most isolated profile identified in the study, in which older adults neither contribute to nor draw from a support network.

As with any cross-sectional study, important caveats apply. The design captures a single moment in time, so it cannot establish whether self-disclosure causes richer support or whether supportive relationships simply create more opportunities for disclosure; most likely, the two reinforce each other in an ongoing loop. The sample comprised participants in a community health screening program, a group that may be more health-conscious and better connected than the general older population, raising questions about generalizability that the authors themselves acknowledge. Moreover, the data are not publicly available because they contain information that could compromise participant privacy, which limits independent reanalysis. The research was approved by the Sapporo Medical University Ethical Review Board, and all participants provided written informed consent after the procedure was explained. Funding came from the Health, Labour and Welfare Sciences Research Grants under grant number 23GB2001.

Future research, the authors suggest, should test these relationships longitudinally and, more ambitiously, develop interventions designed to promote self-disclosure among older adults. If the findings hold up under such scrutiny, the practical message is strikingly simple: encouraging older people to talk openly with their families about their feelings, worries, and daily experiences may do more than relieve loneliness in the moment. It may build and sustain the two-way currents of help—given and received—that keep people healthier, more connected, and more secure as they age. In an era when societies worldwide are scrambling to care for unprecedented numbers of older residents, one of the most powerful tools may not be a new service or technology, but a conversation at the kitchen table.

Subject of Research: The association between self-disclosure and the giving and receiving of social support among community-dwelling older adults

Article Title: Role of Self-Disclosure in Receiving and Giving Social Support Among Community-Dwelling Older Adults: A Cross-Sectional Study

Article References: Role of Self-Disclosure in Receiving and Giving Social Support Among Community-Dwelling Older Adults: A Cross-Sectional Study. (n.d.). https://doi.org/10.1007/s12126-026-09671-x

Image Credits: AI Generated

DOI: 10.1007/s12126-026-09671-x

Keywords: social support, self-disclosure, older adults, aging in place, family communication, community-dwelling seniors, cluster analysis, functional capability, social networks, well-being, cross-sectional study, gerontology