Thematic Session 4
The Algorithmic Turn: Social Science in the Age of AI, Machine Learning, and Big Data
Moderator
Dr. Wilfred Luis L. Clamor
Lecturer, Department of Sociology and Anthropology
April 23, 2026 (Thursday)
11:00 AM – 12:30 PM
This subtheme critically examines advanced computational techniques to address social issues. It focuses on the methodological shift brought about by massive datasets and automated analysis.
Topics of Interest: Machine learning applications for public policy, network analysis and social dynamics, computational text analysis (topic modeling, sentiment analysis), large-scale data visualization, and studies on data scraping and API use in research.
Key Question: How are we adapting quantitative methods and analytical frameworks to leverage (and challenge) Big Data sources?
Artificial Intelligence in Human Resources: Perceptions and Experiences of Practitioners in the Philippines
Dr. Jaimee Felice
Caringal-Go
Department of Psychology
Dr. Mendiola Teng-Calleja
Department of Psychology
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Mark Vincent Gutib
Department of Psychology
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Jhio Jan Navarro
Department of Psychology
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Alexander Dereck
Department of Psychology
Artificial Intelligence (AI) is increasingly being utilized in Human Resource Management (HRM), thereby transforming its practice. However, nuanced literature on in its use in developing contexts remains limited. This exploratory study examined how HR practitioners in the Philippines perceive and experience the utilization of AI in their work. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), it adopted a qualitative research design. Eleven practitioners from diverse industries were interviewed, and data were analyzed through reflexive thematic analysis. Findings highlight the interplay of psychological, social, and organizational factors with technical infrastructures in the utilization of AI in HR work. Social and psychological influences such as leadership support and employee resistance, as well as organizational and technical infrastructures including capability development and data security concerns, both enable and constrain AI adoption. Although AI was described by some participants as intuitive and beneficial, challenges regarding competency gaps and fears about its impact on human roles were always reported. By bridging topics in work psychology and AI, this study highlights the interdisciplinary nature of technology adoption in HRM. It demonstrates that AI’s transformative potential depends not only on technical capacity but also on addressing psychological and social barriers.
Cultural Analytics as Foundation in Developing Anti-Discrimination Policies
Dr. Maria Margarita R. Lavides
UP Center for Integrative and Development Studies​
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Vladimer B. Kobayashi, PhD
UP Center for Integrative and Development Studies​​
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Cristabel F. Tiangco
UP Center for Integrative and Development Studies
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Purisima P. Panlilio, PhD
UP Center for Integrative and Development Studies
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Remi E. De Leon
UP Center for Integrative and Development Studies
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Angela D. Carreon
UP Center for Integrative and Development Studies
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Pakikipagkapwa and discrimination are two-sides of the same coin. While the former emphasizes inclusion and empathy, the latter highlights exclusion and bias. This paper made an in-depth investigation of the Filipino concept of pakikipagkapwa or treating others as fellow human being, using data science methods. The researchers scraped data from various virtual chat groups and online communities and the gathered data were preprocessed and analyzed using the topic modelling technique. Results revealed that the phenomenon of pakikipagkapwa has several dimensions namely, channels, moderators, manifestations, and enablers. Channels refer to modes through which pakikipagkapwa is expressed. Moderators are factors which affect the extent that pakikipagkapwa is exercised. Manifestations are the tangible representations of pakikipagkapwa. Enablers are the catalysts of pakikipagkapwa. Aside from presenting an emergent model of pakikipagkapwa, this work also unearthed the various factors that are relevant in understanding the prevalence of discriminatory behaviors in the Philippines i.e., family upbringing, religious beliefs and practices, education experience, and influence of famous personalities. Awareness of such factors can guide government decisionmakers in developing policies that can address the proliferation of biases which promote discriminatory practices in the country.
Social Representations of Suicide in Filipino Facebook Discourse
Gutsdozer E.
Tancio, RPM, RPSY
Department of Psychology
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Arsenio Sze
Alianan, Jr., PhD
Department of Psychology
Suicide is a global public health concern that continues to affect people of all ages, yet there remains a lack of empirical understanding of how Filipinos collectively make sense of it in everyday public discourse that is grounded in Social Representations Theory, particularly in digital spaces such as Facebook.
This study employed a sequential mixed-methods design, and 5,682 public comments from 46 suicide-related news posts published between 2023 and 2024 by 15 verified Philippine-based Facebook news pages were analyzed. Quantitative topic modeling was first conducted to identify dominant lexical clusters, followed by qualitative interpretive analysis to derive overarching social representations.
Findings revealed four social representations of suicide: faith-moralized, socio-affective, systemic–structural, and psychological–Individualistic. These representations shape social responses to suicide by regulating judgment, empathy, blame, and calls for intervention, underscoring the importance of integrating culturally grounded social representations into suicide prevention and mental health policy in the Philippine context.​
Inflation Dynamics in the Philippines: Evidence from Oil Prices and Text-Based Sentiment Analysis
Dino Carlo A. Saplala
Department of Economics
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Gerald Gracius Y. Pascua
Department of Economics
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Jon Ethan Lancelot
M. Batara
Department of Economics
This study investigates how inflation sentiment in the Philippines, as expressed in business news media, relates to Brent crude oil prices and inflation dynamics as captured in available datasets and surveys of the Bangko Sentral ng Pilipinas (BSP) and Social Weather Stations (SWS). A two-part research methodology that integrates natural language processing (NLP) with applied economics and sentometrics literature is proposed.
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First, a collection of text data in the form of headlines from available Philippine business news media is created. Using dictionary-based analysis and other suitable NLP models, sentiment scores are assigned for each observation in the text data. An inflation sentiment index is created from this part.
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The second part of the study features regressions and correlation analyses of inflation data on the inflation sentiment index from part 1, along with Brent oil prices and other macroeconomic control variables as guided by rational expectations theory. This interdisciplinary study contributes both to NLP through the study of suitable methods for sentiment measurement and to monetary policy through the higher frequency analysis of public sentiment. This provides faster synchronization of policy expectations and potential household and firm behavior.