The study surveyed 220 adults aged 20 to 49 during 2024, using a quantitative, descriptive, non-experimental, and cross-sectional design. Participants were recruited through non-probability convenience sampling, with the online self-administered questionnaire distributed via LinkedIn, WhatsApp groups, and Facebook communities dedicated to job search and employability. The 27-item instrument covered social media use for job hunting, perceived effectiveness compared with traditional methods, platform-specific habits, demographic and professional factors, and the kinds of employment-related content that attract the most attention. Data were processed in Microsoft Excel and analyzed with descriptive statistics, including frequencies and percentages.

The headline number is striking: 77.7 percent of respondents reported having used a social platform to search for work, while only 22.3 percent had not. Even more telling, 78 percent said they personally know someone who landed a job through social media, a figure that reinforces the perception of these platforms as effective mechanisms of labor intermediation. The researchers note that this convergence between indirect success stories and favorable assessments suggests that exposure to positive outcomes in one’s social circle is associated with a more positive view of social media as an employability tool.

Yet the picture is far from uniformly optimistic. When asked to rate social media against traditional job-search methods, exactly half of the sample described the platforms as effective or very effective, with 37.27 percent choosing effective and 12.72 percent very effective. The other half was split between neutral positions, at 28.63 percent, and negative assessments, at 21.35 percent. This divided verdict, the authors argue, reveals that perceived effectiveness is differentiated rather than universal, and it hints that the value of a platform depends heavily on what kind of content flows through it and how skillfully users navigate it.

Platform preferences followed clear functional lines. Facebook emerged as the most frequently used network for job search, prized for its employment-related groups and ease of interaction, making it a natural channel for mass-reach recruitment of operational and administrative profiles. LinkedIn, by contrast, was associated with specialized professional vacancies and networking, consistent with its established role in executive and expert hiring. Instagram occupied an emerging middle ground, valued for its visual dynamism and closeness to brands, which positions it as a growing venue for employer branding and the presentation of organizational culture through dynamic formats. The authors conclude that these platforms should not be understood as interchangeable tools but as communication environments with distinct communicative logics.

Demographic patterns added further texture. The 25-to-34-year-old age group proved to be the most active segment in using social media for employment purposes, reflecting documented tendencies among younger users who show greater familiarity with digital environments. Education also mattered: participants with university education tended to gravitate toward professional platforms such as LinkedIn, while those with secondary or technical education mainly relied on mass-reach networks such as Facebook. Respondents with more than five years of work experience showed a stronger preference for corporate and professional platforms, reinforcing the idea that platform choice tracks the specialization of a person’s occupational profile.

What kind of content actually draws job seekers in? The clearest answer came from the question of what motivates interaction with employment-related posts. A clear and direct message dominated, cited by 72.27 percent of participants, followed by the relevance of the offer at 43.18 percent and attractive visual design at 39.09 percent. When asked which aspects of social media were most effective for job search, access to updated information led decisively with 51.36 percent of mentions, ahead of expanding one’s network of contacts at 30.90 percent and ease of use at 16.81 percent. Elements tied to professional projection, such as greater profile visibility and networking opportunities, trailed far behind. Participants also singled out testimonial videos and publications with transparent information about salaries, benefits, and requirements as the most valued formats.

The findings carry theoretical weight as well. Drawing on Pierre Bourdieu’s concept of social capital, the authors argue that digital platforms do not replace social capital but reconfigure how it is produced, circulated, and mobilized in contemporary labor markets. Manuel Castells’ notion of the network society frames the results as part of a broader shift toward algorithmic models of labor intermediation, in which recruitment increasingly depends on data, segmentation, and predictive analysis rather than the simple publication of vacancies. The study also acknowledges that visibility on social media is conditioned by algorithms that prioritize engagement, meaning that job search is not a neutral process but one structured by technological criteria that influence which opportunities are shown, to whom, and how frequently.

The researchers are careful about the limits of their evidence. Because the sample was recruited through digital channels and selected by convenience, participants likely possessed a minimum level of digital access and familiarity, which may have inflated their favorable assessments. The study therefore captures the perceptions of digitally active users and may underrepresent people with limited digital skills, lower connectivity, or weaker familiarity with employment platforms. The authors also note that positive perceptions of social media may partially reflect users’ pre-existing digital capital, including information-search abilities, profile management, and networking skills, rather than the intrinsic effectiveness of the platforms themselves. Given the descriptive design and non-probability sampling, the findings should be read as patterns within the analyzed sample, not as generalizations about the Costa Rican population.

For employers and recruiters, the practical message is that one-size-fits-all cross-posting is a losing strategy. On mass-reach networks such as Facebook, the authors recommend prioritizing informational clarity, attractive visual formats, and direct calls to action that facilitate applications. On LinkedIn, the emphasis should fall on organizational reputation through specialized content, thought leadership, and networking strategies. Instagram, with its visual and narrative logic, can support recruitment marketing through testimonial content and stories that make employment opportunities more accessible and engaging. As digital skills become a structural condition of employability in a country where roughly 90 percent of the population uses social media frequently, the study suggests that the gap between those who can strategically navigate these platforms and those who cannot may increasingly shape who gets hired, and who gets left behind.

Subject of Research: The use and perceived effectiveness of Facebook, Instagram, and LinkedIn for job search among digitally active adults in Costa Rica

Article Title: The impact of social media on job search in Costa Rica: an analysis from digital communication

Article References: Ulate-Vindas, C., Salazar-Aguilar, J. P., Castillo-Alfaro, N., & Carbonell-Parés, M. A. (2026). The impact of social media on job search in Costa Rica: an analysis from digital communication. Frontiers in Communication, 11, Article 1927812. https://doi.org/10.3389/fcomm.2026.1927812

Image Credits: AI Generated

DOI: 10.3389/fcomm.2026.1927812

Keywords: social media, job search, Costa Rica, digital communication, recruitment, LinkedIn, Facebook, Instagram, employability, employer branding, digital skills, labor market

The research team, Erkut Altindağ of Doğuş University and Özge Gül of Istanbul Rumeli University, collected 42,852 public LinkedIn comments from January to December 2023. They targeted discussions on posts tagged with keywords such as compensation, benefits, salary, and total rewards, along with conversations in professional human resources groups and responses to corporate announcements about benefits packages. Rather than relying on the structured questionnaires and interviews that have long dominated compensation research—methods vulnerable to social desirability bias and flawed retrospective recall—the researchers tapped into unfiltered, spontaneous discourse. They are careful, however, to frame social media as a complement to surveys rather than a replacement, acknowledging that LinkedIn participation is shaped by self-selection, vocal minorities, and the performative nature of public posting.

Technically, the study deployed a multi-modal analytical pipeline built on some of the most powerful tools in modern computational linguistics. At its core was a BERT-based sentiment analysis model, fine-tuned on 1,000 manually labeled compensation-related comments to classify each remark as positive, neutral, or negative. The BERT architecture, a deep bidirectional transformer encoder, outperformed simpler baselines including a TF-IDF logistic regression model (F1 = 0.71) and a lexicon-based VADER approach (F1 = 0.65), achieving a validation accuracy of 0.89 and an F1-score of 0.87 on the test set. Class imbalance in the training data was handled through class-weighted cross-entropy loss, and the framework incorporated a custom lexicon of compensation-specific terms to sharpen accuracy in this particular domain.

Alongside sentiment analysis, the researchers applied two complementary techniques for uncovering structure in the discourse. Topic modeling was performed using both Latent Dirichlet Allocation, optimized to 15 topics through coherence scoring, and BERTopic, a neural approach capable of tracking how themes evolved dynamically across the year. In parallel, a custom named entity recognition model built on spaCy’s framework and trained on 2,500 annotated comments extracted specific entities from the text: organizations, benefit types, and job roles. The entity recognition model achieved an F1-score of 0.83, with particularly strong performance in identifying organizations (F1 = 0.86) and benefit types (F1 = 0.83). Data quality was protected through rigorous preprocessing—English-language filtering using langdetect, deduplication via exact and fuzzy matching based on Levenshtein distance, and thorough text cleaning—while validation included inter-rater reliability assessment by three independent coders, who achieved a Cohen’s Kappa of 0.82, and five-fold cross-validation across all models.

The results paint a vivid picture of sector-specific sentiment in the world of work. Technology sector discussions maintained the highest positive sentiment, with a mean score of 0.42, followed by healthcare at 0.31 and financial services at 0.25. A one-way analysis of variance confirmed that these differences between industries were statistically significant (F(2, 42,849) = 23.45, p < 0.001). Sentiment also fluctuated throughout the year, with notable peaks coinciding with major corporate benefits announcements, and the technology sector showed greater volatility than other industries. The authors note that because LinkedIn comments are nested within users, posts, and organizations, the standard ANOVA violates independence assumptions, so the reported statistic should be treated as a first-order approximation, with multilevel modeling recommended for future work.

Perhaps the most striking finding concerns how the themes of employee conversation shifted over the course of a single year. Work-life balance discussions surged from 14.2 percent of the discourse in the first quarter of 2023 to 41.2 percent by the fourth quarter—a 27 percentage-point increase in thematic prevalence. Meanwhile, traditional base compensation discussions declined by 12.0 percent. A chi-square test confirmed the statistical significance of the overall thematic shift (χ²(4) = 156.23, p < 0.001), though the researchers emphasize that the omnibus test does not establish the significance of any single theme’s change; the individual percentage shifts are presented as descriptive magnitudes pending confirmatory pairwise comparisons with multiple-testing correction. Even with those caveats, the direction is clear: employees are talking less about raw salary and more about time, flexibility, and well-being.

The named entity recognition analysis added another layer of insight by linking specific benefits to sentiment outcomes. The model identified 3,427 unique organizations, 892 granular benefit-related entities—which aggregate onto roughly 47 canonical benefit categories—and 1,245 job roles across the dataset. Health insurance emerged as the benefit type most strongly correlated with positive sentiment (r = 0.42, p < 0.001, 95 percent confidence interval 0.38 to 0.46), followed closely by flexible work arrangements (r = 0.38, p < 0.001). These correlations remained robust after controlling for industry sector and temporal variation. Health insurance dominated the discourse with 12,458 mentions, while retirement benefits, despite lower frequency at 7,892 mentions, maintained a moderate positive correlation with sentiment (r = 0.31, p < 0.001).

The theoretical backbone of the study is Social Exchange Theory, the classic framework introduced by Peter Blau in 1964, which holds that workplace relationships operate on reciprocity: employees weigh the benefits they receive against the effort and commitment they contribute. When the exchange feels fair, workers respond with engagement, loyalty, and discretionary effort; when they feel undervalued, dissatisfaction and turnover intentions follow. Viewed through this lens, the findings suggest that employees increasingly interpret non-monetary rewards—flexible schedules, development opportunities, and work-life balance initiatives—as signals of organizational commitment to their well-being, not merely as transactional extras. The observed migration of discourse away from traditional compensation and toward holistic well-being aligns with this relational interpretation and with prior survey-based evidence that workers increasingly prioritize intangible benefits over pay alone.

The authors are candid about the limitations of their approach. The single-platform focus on LinkedIn may miss perspectives prevalent elsewhere; the one-year observation window means the quarter-to-quarter thematic shifts should be read as within-year movements rather than stable longitudinal trends; the English-only filter introduces cultural and linguistic bias; and LinkedIn’s user base skews toward certain professional and demographic groups. The dataset itself, consisting of publicly available comments collected under applicable data protection rules, cannot be shared in raw form, though anonymized and aggregated data are available on reasonable request. Despite these constraints, the study establishes a validated, replicable framework for social media analytics in compensation research—one the authors say achieved robust overall performance (F1 = 0.83) across the pipeline.

The practical implications for employers are considerable. As organizations compete for talent in a tight labor market, the study suggests that total rewards strategies built around pay alone may be increasingly out of step with workforce expectations. Compensation professionals now have evidence that flexible work arrangements and health benefits generate the strongest positive sentiment, that sentiment varies meaningfully by industry, and that the timing of benefits announcements visibly moves the needle on employee discourse. The researchers call for cross-platform validation, longitudinal studies spanning multiple years, multilingual analysis capabilities, and the integration of demographic variables to capture preference variation across employee segments. They even point toward predictive models capable of anticipating emerging compensation trends before they fully materialize in public conversation. In an era when employee voice is amplified, searchable, and machine-readable, the silent signals of the workforce are silent no more—and organizations that learn to listen computationally may gain a decisive edge in designing rewards that resonate.

Beyond its immediate findings, the study sits within a broader methodological turn in organizational research. Computational text analysis has gained traction across management science because it captures behavior in natural settings, sidestepping the artificiality of laboratory tasks and the recall problems of retrospective questionnaires. The choice of BERT is significant in this respect: unlike earlier bag-of-words techniques that ignore word order, transformer models process each word in relation to its surrounding context, allowing them to distinguish, for example, a sarcastic complaint about a benefits package from a sincere endorsement using nearly identical vocabulary. This contextual sensitivity matters greatly in compensation discourse, where negation, hedging, and irony are common.

The domain-specific adaptations the researchers made also illustrate a key lesson for applied text analytics. Off-the-shelf sentiment tools are typically trained on general web text or product reviews, where the language of workplace compensation is underrepresented. By supplementing the model with a custom lexicon of compensation terms and fine-tuning on manually labeled comments, the team addressed the vocabulary gap that often degrades performance when general-purpose models are applied to specialized professional discourse. The reported gap between the BERT model and the lexicon-based VADER baseline underscores how much accuracy can be lost without such adaptation.

The theoretical framing also deserves emphasis. Social Exchange Theory, as elaborated by scholars such as Gould-Williams and Davies, holds that employees interpret rewards not merely as transactional payments but as signals of how much the organization values them. The study’s finding that health insurance and flexible work arrangements carry the strongest positive sentiment fits this account: benefits that touch on security and personal autonomy may function as especially potent signals of organizational care. Conversely, the decline in base-pay discussion suggests that salary, while foundational, may be increasingly treated as a baseline expectation rather than a differentiator among employers.

For researchers, the study also highlights unresolved measurement questions. Because sentiment scores were aggregated across comments nested within users, posts, and organizations, the effective sample size for industry comparisons is smaller than the raw comment count suggests, and future multilevel designs could partition variance at each level. Extending the framework to multilingual corpora would be particularly valuable given that compensation norms and benefit expectations vary substantially across national labor markets. If subsequent work confirms these patterns across platforms and languages, social media analytics could become a routine complement to engagement surveys, giving organizations a near real-time barometer of how their reward strategies are actually landing with the workforce.

Subject of Research: Natural language processing analysis of employee perceptions of total rewards through LinkedIn discourse

Article Title: Employee perceptions of total rewards revealed through natural language processing of LinkedIn discourse

Article References: Altindağ, E., & Gül, Ö. (2026). Employee perceptions of total rewards revealed through natural language processing of LinkedIn discourse. Discover Global Society, 4(1), Article 238. https://doi.org/10.1007/s44282-026-00565-6

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00565-6

Keywords: natural language processing, total rewards, LinkedIn, sentiment analysis, employee benefits, compensation, social media analytics, BERT, topic modeling, named entity recognition, work-life balance, human resource management