Text-Mining Technique To Identify Ageist Online Jobs Ads In The Philippines - Manahan, Fernan Dayson C. (2024)
ABSTRACT

The concept of ageism or age-based discrimination was introduced ion 1969, but it remains highly relevant in the present, as increased life expectancy brings together people from various generations and various ages into the same society and same social settings, including the workforce. Similar to allegations of other types of discrimination, however, determining if ageism has occurred appears largely subjective and anecdotal, especially in written text, if one does not simply based it on the actual presence of prohibited words and phrases. For instance, a job advertisement may not literally say the job is for "a young person" or "people in their 20s, "but it may use phrasing that is akin to off-putting statements such as "recent graduate" or "technology savvy". resulting in a similar impression and effectively deterring the same persons of age.

The study uses text-mining techniques to quantitatively determine if a particular job advertisement is ageist. Text of variable length--whether they are singular words, phrases, or entire sentences or paragraphs--are given certain numerical representations. These numerical representations are then used: (1) to determine how similar a certain suspect word or phrase is to an explicitly prohibited or prelabeled ageist word or phrase; or (2) to predict if a particular suspect text is ageist or not.

Using Documents Embeddings, a piece of text (words, phrases, sentences, or paragraphs; generally referred to as documents) is represented as a vector. As vectors, documents that are dissimilar to them. This provides a means to measure (through vector distance) how similar a document is to a known ageist text.

Because of their numerical vector representations, documents can also be clustered, such that documents whose ageism is yet to be determined --for instance, job ads (and the phrases or n-grams in their contents)--could belong to a cluster with a high prevalence of known or prelabeled ageist documents. Thus, the contents of 8,918 online job advertisements--containing 1,886,910 trigrams (or three-word phrases)--between August 2022 and November 2023 were scraped from four well-known job ad website in the Philippines (Kalibrr, Jobstreet, Indeed and Linkedln). On the other hand, in order to build models, this study also collected 14,367 English words, phrases, and statements prelabeled as ageist or not from prior ageism-related studies in the Philippines, United Kingdom, United States, and worldwide. This study's clustering experiments suggest that 1,050 scraped job ads (11.8%) could be ageist, because they belonged to a cluster where a majority (60%) of the other documents in the cluster were known ageist text, particularly the negative stereotype about older people and technology.

Alternatively, through the popular Latent Dirichlet Allocation (LDA) method, documents are assigned additional numerical values correspending to their probability of belonging to certain groups or topics. These topic assignments along with the document's vector representations, are used as features for ageist-or-not classification models, such as logistic regression and penalized ridge regression. However, this study's experimental classification models and proposed method to choose preferable (high-accuracy, low false-positives) and careful (low false positives) models, showed that embeddings-only (no-LDA) classifications models performed better on the holdout set, with an accuracy between 63.5% and 67% and a false-positive rate between 0.1% and 6.2%.

These techniques--gathering prelabeled phrases or documents as training data, creating an ageist-or-not classification models, clustering documents to determine potentially ageist sets of documents--could be applied and further refined by online job advertisements. Age-positive or age-neutral rewrites may then be recommended. Regulators may also use these techniques in shortlisting age-related bias not only in job advertisements but in any text where age discrimination may exist.
Includes bibliographic reference.
Keywords: Ageism -- Age Discrimination -- Document Embeddings -- doc2vec -- text mining -- Latent Dirichlet Allocation -- Job Advertisement