فاطمه عباسی

Objective: Marital infidelity is one of the most significant threats to the stability of marital relationships and a leading cause of divorce across various cultures. This study aimed to examine the effectiveness of cognitive-behavioral therapy (CBT) on self-compassion and experiential avoidance in women who have experienced marital infidelity. Methods: The current research was a quasi-experimental study with a pre-test and post-test design, involving experimental and control groups and a three-month follow-up. All women who had experienced marital infidelity and attended psychotherapy and counseling clinics in Tehran during the year 2021-2022 constituted the statistical population of this study, from which 30 were selected via convenience sampling based on the inclusion criteria and randomly assigned to two equal groups of 15. Data were collected using the Self-Compassion Scale (Neff, 2003) and the Acceptance and Action Questionnaire - Second Version (Bond et al., 2007). Participants in the experimental group received 8 sessions of intervention. Data analysis was conducted using descriptive statistics and one-way analysis of variance with SPSS version 26. Findings: The mean self-compassion score and its dimensions in the experimental group significantly improved post-intervention compared to the control group (p < 0.05). Cognitive-behavioral therapy was beneficial in reducing experiential avoidance in the experimental group, unlike the control group (p < 0.05). The effects of this therapeutic program on dependent variables continued during the three-month follow-up period (p < 0.05). Conclusion: Cognitive-behavioral exercises can be used as an effective intervention method to improve the cognitive-emotional status of women who have experienced marital infidelity.

Social media offers a timely lens into public perceptions of emerging technologies. To assess public opinion on the Internet of Things (IoT), we analyzed a corpus of 824,845 IoT-related posts collected from X between 2013 and 2022. Using Latent Dirichlet Allocation (LDA), we identified seven primary themes of discussion: Smart Home, Business Intelligence, Artificial Intelligence, Smart City, IoT Usage, Emerging Technologies, and Blockchain. We then applied an unsupervised machine-learning technique to evaluate sentiment toward each theme. Overall, public discourse was positive: 46.78% of tweets expressed positive sentiment, 43.41% were neutral, and 9.81% were negative. Although predictable, short-term shifts in tone occurred around specific events, interest in these themes remained consistent throughout the study period. These findings suggest that the Internet of Things is generally perceived favorably and demonstrate how large-scale social media analytics can capture authentic, real-time attitudes toward complex technologies. By linking public opinion to specific topics of discussion, our results provide valuable insights for researchers, policymakers, and product teams seeking to align IoT development with societal expectations.