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Because of their stigmatized social status, sexual and gender minority (SGM; e.g., gay, transgender) people experience minority stress (i.e., identity-based stress arising from adverse social conditions). Given that minority stress is the leading framework for understanding health inequity among SGM people, researchers and clinicians need accurate methods to detect minority stress. Since social media fulfills important developmental, affiliative, and coping functions for SGM people, social media may be an ecologically valid channel for detecting minority stress. In this paper, we propose a bidirectional long short-term memory (BI-LSTM) network for classifying minority stress disclosed on Reddit. Our experiments on a dataset of 12,645 Reddit posts resulted in an average accuracy of 65%.
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Classifying Minority Stress Disclosure on Social Media with
Bidirectional Long Short-Term Memory
Cory J. Cascalheira1,2,
Shah Muhammad Hamdi1,
Jillian R. Scheer2,
Koustuv Saha3,
Soukaina Filali Boubrahimi4,
Munmun De Choudhury5
1New Mexico State University,
2Syracuse University,
3Microsoft Research,
4Utah State University,
5Georgia Institute of Technology
Abstract
Because of their stigmatized social status, sexual and gender minority (SGM; e.g., gay,
transgender) people experience minority stress (i.e., identity-based stress arising from adverse
social conditions). Given that minority stress is the leading framework for understanding health
inequity among SGM people, researchers and clinicians need accurate methods to detect minority
stress. Since social media fulfills important developmental, affiliative, and coping functions for
SGM people, social media may be an ecologically valid channel for detecting minority stress. In
this paper, we propose a bidirectional long short-term memory (BI-LSTM) network for classifying
minority stress disclosed on Reddit. Our experiments on a dataset of 12,645 Reddit posts resulted
in an average accuracy of 65%.
Introduction
Establishing the construct validity of psychological phenomena with computational, data-
driven approaches is a federal funding priority (National Institutes of Health 2021). To
pursue this priority, researchers have used machine learning (ML) and deep learning (DL)
to classify mental health disorders, such as depression (Aldarwish and Ahmad 2017), from
social media posts. Despite these advances, computational support for the construct validity
of
minority stress
(i.e., identity-based stress arising from adverse social conditions; Meyer
2003) is lacking.
cjcascalheira@gmail.com .
HHS Public Access
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27.
Published in final edited form as:
Proc Int AAAI Conf Weblogs Soc Media
. 2022 May 31; 16: 1373–1377.
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Minority Stress and Social Media
Because of their stigmatized social status, sexual and gender minority (SGM; e.g., gay,
transgender) people experience minority stress (Meyer 2003). Minority stress is associated
with poorer health outcomes (e.g., Hatzenbuehler and Pachankis 2016). Consequently,
minority stress is the leading framework for understanding health inequity among SGM
people relative to the general population (Institute of Medicine of the National Academies
2011). Prominent SGM-tailored clinical interventions aim to ameliorate minority stress or
to bolster coping (e.g., Pachankis, McConocha, et al., 2020). To study and treat minority
stress, researchers and clinicians must accurately detect it. However, like most constructs in
psychological science (Sassenberg and Ditrich 2019), the dominant method of detecting
minority stress is via survey-based self-report, which has numerous methodological
limitations (e.g., retrospective reporting; Heppner et al. 2016). While physiological and
observational techniques address these limitations (Heppner et al. 2016), these methods can
be hard to implement (e.g., participant must visit lab) if SGM participants are not out or
live in high-stigma areas. Consequently, hard-to-reach SGM samples often are solicited from
social media (Lunn et al. 2019).
Social media
as a data source
may be an ecologically valid way to detect minority stress
because social media often fulfills important developmental, affiliative, and coping functions
for SGM people (Formby 2017; Tropiano 2014; McInroy and Craig 2020; Woznicki et al.
2021). Compared to 58% of the general public, 80% of SGM adults have used a social
media website (Pew Research Center 2013). Many SGM people solicit social support on
social media (McInroy and Craig 2020). Seeking social support is an effective strategy to
cope with minority stress (e.g., Toomey et al., 2018). To obtain social support online, social
media users are motivated to self-disclose (Luo and Hancock 2020), and research shows that
SGM people disclose minority stress on social media (Saha et al. 2019).
Consequently, using social media to detect minority stress disclosures may circumvent
survey-based limitations, meet the logistical demands of research with SGM people, and
increase understanding of minority stress disclosure within the virtual environment. Large
social media data sets require sophisticated pattern mining techniques. Thus, deep learning
methods, for their end-to-end learning ability from large datasets, have the potential to
complement and to extend minority stress theory and existing SGM research. However,
no study has tested the potential of a particular type of DL—the recurrent neural network
(RNN)—to classify minority stress disclosures on social media. The main contribution of
this proof-of-concept paper is the application of a bidirectional long short-term memory
(BI-LSTM) network to classify minority stress on a social media dataset.
Data Source
We used a existing dataset. Saha et al. (2019) manually labeled 350 (2.77%) of the 12,645
Reddit posts in the data set. The team extracted 659 features (e.g., sentiment, hate speech) of
minority stress from these manually labeled examples, then used the multilayer perceptron
(MLP) algorithm as a classifier to label the remaining Reddit posts. The MLP classifier
identified 4,419 (35%) Reddit posts as exhibiting minority stress (Saha et al. 2019); thus, the
dataset was unbalanced. We used this dataset to examine the performance of BI-LSTM. The
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dataset was split into training (80%), validation (10%), and test (10%) using stratification.
Binary labels for the presence (1) or absence (0) of minority stress were used.
Neural Network Selection and Architecture
To build upon Saha et al. (2019), we investigated a particular type of RNN, the BI-LSTM.
Compared to feedforward neural networks (FFNNs), such as the MLP classifier, RNNs
warrant investigation for several reasons.
First, RNNs are specialized for sequential data (Goodfellow, Bengio, and Courville 2016),
such as textual Reddit posts. Unlike the traditional FFNN, RNNs learn representations given
the temporal relationship of the data points.
Second, similar to FFNNs, RNNs learn parameters via backpropagation. However, when
backpropagation is applied to sequential data, the gradients tend to vanish (i.e., shrink
exponentially) or explode (i.e., grow exponentially), thereby introducing error and impairing
the model’s capability to learn parameters (Hochreiter and Schmidhuber 1997). Essentially,
the output gate learns when “to trap” the error and prevent it from impacting the model,
whereas the input gate learns when “to release” the error (Hochreiter and Schmidhuber
1997, 7). LSTM (and, by extension, BI-LSTM) learns when to keep or to forget
information that improves parameter estimation even when the sequences are substantially
long (Goodfellow, Bengio, and Courville 2016). Therefore, unlike FFNNs, LSTM models
efficiently handle long-term dependencies (Goodfellow, Bengio, and Courville 2016).
LSTM’s ability to remember information over a long sequence period is beneficial for our
work because Reddit posts can be several paragraphs long. Finally, LSTM-based models
have shown excellent performance in classification tasks for related psychological constructs
(Bisht et al. 2020), achieving better accuracy over MLP (Süt and Şenocak 2007).
In this work, we used a BI-LSTM architecture (Cheng 2020) and implemented it in PyTorch
(Paszke et al. 2019). See Figure 1 for the network’s architecture. First, each Reddit post was
transformed into an object, which held the length of each Reddit post and the tokens (i.e.,
individual words). Reddit posts were transformed into tokens using spaCy (Honnibal and
Montani 2021).
Second, a word embedding layer was constructed. The embedding layer is a |
V
| ×
D
matrix where
V
is the vocabulary (i.e., number of tokens in the Reddit post) and
D
is the
dimensionality of the embeddings (
D
= 300; Paszke et al. 2019). Word embeddings are able
to represent the syntactic and semantic information attributed to words (Lai et al. 2016).
Capturing linguistic meaning allows the words in the Reddit post to serve as better input
units for BI-LSTM.
Next, a one-layer BI-LSTM was created with an input tensor of size (
N
,
L
,
H
in), where
N
is
the batch size,
L
is the sequence length, and
H
in is the size of embedding dimension (Paszke
et al. 2019); 128 features were used in the hidden state. BI-LSTM expands the LSTM cell’s
ability to consider
context
in learning representation (Goodfellow, Bengio, and Courville
2016). Context, in text data, indicates how words are influenced by neighboring words.
Unlike traditional LSTM, which can only assess the previous context (i.e., the sequence
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of words in the past), BI-LSTM takes advantages of the future context (i.e., the next
sequence of words) as well. That is, BI-LSTM processes text in both directions, updating
hidden layer activations by moving from the start of the input sentence (i.e., the forward
sequence) as well as from the end of the input sentence (i.e., the backward sequence; Graves,
Mohamed, and Hinton 2013). The BI-LSTM output was concatenated and fed into a dropout
regularization layer with probability 0.5.
Finally, the concatenated and regularized representation from the BI-LSTM layer was passed
to a FCNN (fully connected neural network) with a sigmoid activation function to obtain the
probability that a Reddit post evinces minority stress. The FCNN layer has size 256 (i.e., 2 *
128, the number of features in the hidden state).
During training and evaluation, cross-entropy loss and the Adam optimizer were used.
Cross-entropy loss is appropriate for this binary classification problem. The Adam optimizer
adapts the learning rate throughout training (Goodfellow, Bengio, and Courville 2016) and
the following values were initialized: learning rate = 1e-3,
β
1 = 0.9,
β
2 = 0.999,
ε
= 1e-8.
Classification Results
Hand-coded hyperparameter tuning was used. Only the batch size, learning rate (i.e., alpha),
and number of epochs were tuned. Six sets of hyperparameters were examined and are
presented in Table 1. Figure 2 shows the training and validation loss. Results indicated that
accuracy and the F1 scores were greatest for hyperparameter set 1 due to a longer training
time. As shown in hyperparameter Set 3, increasing the alpha reduced the accuracy and
F1 scores. Compared to Set 2, increasing the batch size in hyperparameter Set 4 did not
influence the accuracy. We reduced the epochs in attempt to address the overfitting evident
in hyperparameter Set 1, but reducing epochs to 3 (Set 5) resulted in inferior accuracy.
Thus, although the accuracy and F1 scores were not superior in Set 2, we used these
hyperparameters in Set 6 to evaluate our model.
During evaluation, the one-layer BI-LSTM model correctly classified 65.34% of the Reddit
posts with F1 scores falling from training (1 = 0.11, 0 = 0.78). As shown in Figure 3, the
model generated many false negatives.
Since unbalanced datasets can affect accuracy (Goodfellow, Bengio, and Courville 2016),
we trained the BI-LSTM model again using hyperparameter Set 6 with a balanced dataset.
We randomly sampled negative cases of minority stress disclosure to match the number of
positive cases (
n
= 4,419) to establish a 1:1 ratio (
N
= 8,838).
Although the accuracy fell to 61.55% with the balanced dataset, BI-LSTM detected more
true positive and true negative cases of minority stress (see Figure 4).
Discussion
This proof-of-concept paper provided initial evidence in support of using sequence models
(i.e., BI-LSTM) in classifying minority stress disclosures on social media. Although BI-
LSTM (65%) achieved a lower accuracy than MLP (75%; Saha et al. 2019), several
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limitations must be considered to contextualize the results. Importantly, this paper opens
the door to using deep neural networks for studying minority stress which, as a theory-driven
concept, is novel given that no features were engineered by experts in this study.
Limitations and Future Research
To build upon this initial study, the following limitations should be addressed. First, ad-hoc
hyperparameter tuning was performed. A more rigorous method would be the use of random
search for hyperparameter selection. Second, and relatedly, only three hyperparameters were
tuned. Future work should consider tuning additional hyperparameters, such as increasing
the number of BI-LSTM layers. Third, although the BI-LSTM model performed adequately
with custom word embeddings, future work should consider phrase or sentence embeddings.
Indeed, minority stress is a nuanced psychological construct that is difficult to communicate
without using multiple words. As the results indicate, single-word embeddings appeared
unable to capture the semantic and syntactic meaning of minority stress. Fourth, if single-
word embeddings are pursued, then instead of custom word embeddings, future work should
consider pretrained embeddings, such as Word2Vec, FastText, and GloVe. Finally, we should
examine other DL techniques for text classification, such as convolutional LSTM (Zhou et
al. 2015) or bidirectional encoder representations from transformers (BERT; Devlin et al.
2019).
Broader Perspectives and Ethics
Despite these limitations, detecting minority stress on social media with sequence models
warrants further research. If sequence models can accurately classify minority stress
disclosure, then their deployment as services could (a) identify SGM Internet users most at
risk for adverse consequences; (b) link SGM people to professional care (e.g., personalized
ads to SGM-affirming therapists); and (c) generate brief, automated interventions (e.g.,
chatbots to affirm disclosure, screen for comorbid risks, and link to resources). Without
addressing the above limitations, definitive conclusions about the potential of sequence
models to classify minority stress on social media remain elusive.
Several ethical considerations are worth mentioning. First, SGM people may use social
media for relative anonymity. If algorithms identify posts as evincing minority stress, then
SGM users may be targeted by malicious others. Relatedly, if an SGM user discloses
minority stress that is based on or political injustice, then it is possible that the user could be
identified by the perpetrator. We minimized these risks by using Reddit, which is relatively
more anonymous than services such as Facebook. Second, although SGM people consent to
third parties accessing their information by signing up for Reddit, it is neither clear whether
SGM users would consent to the computational classification of their content nor whether
they would appreciate the algorithmic interventions proposed above. As Saha et al. (2019)
concluded, it is imperative that minority stress classification from social media data does no
harm and benefits the SGM community.
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Figure 1.
BI-LSTM network architecture.
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Figure 2.
Training and validation loss, unbalanced dataset.
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Figure 3.
Classification performance, unbalanced dataset.
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Figure 4.
Classification performance, balanced dataset.
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Cascalheira et al. Page 12
Table1.
Training and validation loss, unbalanced dataset.
Set Hyperparameters Acc. F1, 1 F1, 0
1 batch (4), alpha (0.001), #epochs (10) 0.89 0.83 0.92
2 batch (4), alpha (0.001), #epochs (5) 0.82 0.73 0.87
3 batch (4), alpha (0.01), #epochs (5) 0.64 0.41 0.74
4 batch (10), alpha (0.001), #epochs (5) 0.82 0.72 0.87
5 batch (10), alpha (0.001), #epochs (3) 0.76 0.60 0.82
6 batch (4), alpha (0.001), #epochs (5) 0.81 0.71 0.86
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Objective: To remedy the notable gap in evidence-based treatments for sexual minority women, this study tested the efficacy of a minority-stress-focused cognitive-behavioral treatment intended to improve this population's mental and behavioral health. Method: The intervention, EQuIP (Empowering Queer Identities in Psychotherapy), was adapted from a transdiagnostic cognitive-behavioral treatment as also recently adapted for sexual minority men. Sexual minority women at risk of mental and behavioral health problems (n = 19) and expert providers with this population (n = 12) shaped the treatment's development, including by supporting its primary focus on universal and minority-stress-focused processes underlying this population's disproportionately poor mental and behavioral health. The resulting treatment was then delivered to young adult sexual minority women (n = 60; M age = 25.58; 41.67% racial/ethnic minority; 43.33% transgender/nonbinary) experiencing depression/anxiety and past 90-day heavy alcohol use. Results: Compared to waitlist (n = 30), participants randomized to immediately receive EQuIP (n = 30) experienced significantly reduced depression and anxiety (d = 0.85, 0.86, respectively); effects for alcohol use problems were smaller (d = 0.29) and marginally significant. In pre- to post-intervention pooled analyses, effect sizes for minority stress processes (mean d = .25) and universal risk factors (mean d = .48), through which the treatment was expected to work, were small and moderate, respectively, and in the expected direction. Conclusions: This study provides initial support for a minority-stress-focused transdiagnostic cognitive-behavioral treatment for sexual minority women. These first results can launch exploration of other mechanisms and modalities through which to equip this population with evidence-based support. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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In today’s world, internet is an emerging technology with exponential user growth. A major concern with that is the increase of toxic online content by people of different backgrounds. With the expansion of deep learning, quite a lot of researches have inclined toward using their deep neural networks for abundant discipline. Even for natural language processing (NLP)-based tasks, deep networks, specifically recurrent neural network (RNN), and their types are lately being considered over the traditional shallow networks. This paper addresses the problem of hate speech hovering on social media. We propose an LTSM-based classification system that differentiates between hate speech and offensive language. This system describes a contemporary approach that employs word embeddings with LSTM and Bi-LSTM neural networks for the identification of hate speech on Twitter. The best performing LSTM network classifier achieved an accuracy of 86% with early stopping criterion based on loss function during training.
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Self-disclosure is pervasive on social media and has significant implications for psychological well-being. In this review we synthesize recent research on the motivations, mechanisms and effects of self-disclosure on well-being and then propose a framework that highlights the bidirectional relationship between self-disclosure and well-being. The framework details the mechanisms by which self-disclosure on social media can influence well-being and how self-disclosure fulfills particular needs of individuals with different well-being characteristics. We call for future research to examine the proposed bi-directional relationship, especially studies designed to tease out causal effects.
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