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How People Feel About Antipsychotics & Mood Stabilizers: Twitter/X Sentiment Analysis (2024 Study)

Social media has emerged as a pivotal source of real-world evidence, especially in understanding patient experiences and perceptions regarding the treatment of severe mental disorders such as Schizophrenia and related psychotic disorders (SRD) and Bipolar Disorder (BD).

Leveraging advanced artificial intelligence (AI) techniques, a comprehensive analysis of 893,289 tweets between 2008 and 2022 revealed significant insights into the public discourse surrounding the pharmacological treatment of these conditions.

This study underscores the potential of social media listening in bridging the gap between clinical practice and patient experiences, offering a unique window into the societal and emotional underpinnings of medication adherence and public perception.


  1. Extensive Data Analysis: 893,289 tweets related to SRD and BD treatment were analyzed using advanced AI techniques, uncovering patterns in medication mentions and associated emotions.
  2. Diverse Medication Discussions: Second-generation antipsychotics were predominantly mentioned in English tweets, while mood stabilizers were more common in Spanish tweets, highlighting cultural and linguistic differences in public discussions on mental health treatments.
  3. Emotional Insights: The analysis revealed distinct emotional tones in tweets, with English tweets about economic and legal issues expressing negative emotions, and Spanish tweets on medication shortages evoking anger, indicating varied concerns across languages.
  4. Social Media as a Research Tool: The study demonstrates the value of social media research in understanding patient and public perceptions, potentially guiding clinicians towards more patient-centered care.

Source: Journal of Affective Disorders (2024)

Findings: Sentiment Analysis of Antipsychotics & Mood Stabilizers via Twitter/X (2024)

The study’s findings offer a comprehensive insight into the public discourse surrounding the pharmacological treatment of severe mental disorders, specifically through the lens of Twitter.

1. Frequency of Mentions & Language Differences

Second-Generation Antipsychotics vs. Mood Stabilizers

The analysis of 893,289 tweets revealed that second-generation antipsychotics were more frequently mentioned in English tweets.

This includes medications such as Quetiapine, Risperidone, and Aripiprazole, indicating a significant discussion surrounding these drugs among English-speaking users.

Conversely, Spanish tweets showed a higher frequency of mentions for mood stabilizers, including Lamotrigine, Carbamazepine, and Lithium, suggesting different treatment preferences or experiences among Spanish-speaking communities.

Temporal Trends

A notable increase in tweets mentioning these medications was observed over the study period, with a significant rise post-2020.

This trend aligns with the global increase in mental health issues and changes in prescription patterns due to the COVID-19 pandemic.

2. Thematic Content & Emotional Analysis

Economic and Legal Aspects: English tweets often highlighted concerns related to the economic and legal aspects of mental health treatments. These discussions displayed predominantly negative emotions, such as fear and sadness, reflecting concerns over the affordability and legality of medications.

Advice Seeking and Medication Shortages: In contrast, Spanish tweets frequently sought advice regarding treatments and highlighted issues related to medication shortages. These tweets evoked strong feelings of surprise and anger, underscoring the impact of accessibility issues on patients’ emotional well-being.

Recurring Themes: Among Spanish tweets, a significant recurring theme was the shortage of medications, which not only generated discussions about the practical challenges faced by patients but also evoked feelings of anger, pointing to a critical area of concern for healthcare systems in Spanish-speaking regions.

3. Medication Discussions

Quetiapine, Risperidone, Aripiprazole: These medications garnered the highest number of mentions among English tweets, with Quetiapine being the most discussed drug overall. This indicates a broad public interest and potentially reflects the widespread use of these medications in English-speaking countries.

Lithium: Notably, Lithium saw a significant increase in mentions since 2020. Discussions around Lithium were particularly focused on its role in the treatment of Bipolar Disorder, with the increase in mentions possibly related to the ongoing debates about its underutilization and side effects.

Drug Shortages: Spanish tweets specifically highlighted the issue of drug shortages, particularly for mood stabilizers like Carbamazepine, which saw peaks in discussion corresponding to reported shortages in various Spanish-speaking regions.

4. Sentiment Analysis (Emotional Landscape)

Negative Emotions in Economic & Legal Discussions: The analysis found that tweets in English discussing economic and legal issues associated with mental health treatments predominantly conveyed negative emotions. This suggests a significant level of concern and discontent among patients and the public regarding the financial and regulatory landscape of mental health care.

Anger & Surprise in Spanish Tweets: Spanish tweets discussing medication shortages and seeking advice displayed emotions of anger and surprise. This reflects the frustration and unexpected challenges faced by patients in accessing their medications, as well as the emotional impact of navigating treatment options and shortages.

Analysis of Antipsychotics & Other Psychiatric Medications via Twitter/X Posts (2024 Study)

J P Chart-Pascual et al. leveraged social media, specifically Twitter, as a resource to better understand patient experiences, preferences, and perceptions regarding the pharmacological treatment of severe mental disorders, such as Schizophrenia and related psychotic disorders (SRD) and Bipolar Disorder (BD).

The main objectives were to:

  • Investigate the frequency and nature of online communications about pharmacological treatments used for SRD and BD among Twitter users from 2008 to 2022.
  • Determine the primary thematic content of these Twitter posts.
  • Analyze the emotions associated with these tweets to gain insights into patient and public sentiment regarding these treatments.


The study utilized advanced artificial intelligence techniques, encompassing machine learning (ML), deep learning (DL), and natural language processing (NLP), to analyze a dataset of 893,289 tweets posted between 2008 and 2022.

These tweets mentioned the names of main drugs used in the treatment of SRD and BD, including both their generic names and marketed brand names in English and Spanish. The analysis process involved:

  • Collecting tweets using specific keywords related to SRD and BD medications.
  • Preprocessing the data to remove irrelevant text elements and categorizing tweets based on language.
  • Employing unsupervised learning techniques, specifically latent Dirichlet allocation (LDA), for topic modeling to identify the main themes within the tweets.
  • Conducting sentiment analysis using advanced models to categorize tweets into emotions such as anger, joy, surprise, etc., to understand the emotional context of the discussions.


  • Frequency & Language Differences: Second-generation antipsychotics were more frequently mentioned in English tweets, while mood stabilizers were more common in Spanish tweets, indicating linguistic and possibly cultural differences in the discussion of mental health treatments.
  • Thematic Content: English tweets often discussed economic and legal aspects of treatments, displaying predominantly negative emotions. Spanish tweets, on the other hand, frequently addressed issues of medication shortage and sought advice, evoking strong feelings of anger and surprise, respectively.
  • Emotional Analysis: The sentiment analysis showed that tweets about economic and legal issues in English expressed fear and sadness, whereas Spanish tweets about drug shortages were marked by anger. This indicates varied emotional responses to different aspects of mental health treatment across languages.
  • Medication Discussions: The study also found a significant increase in the mention of certain medications, such as second-generation antipsychotics and Lithium, over the study period, reflecting changes in prescription patterns and public interest, possibly influenced by factors like the COVID-19 pandemic.


  • Platform Demographics: The analysis of Twitter data, while insightful, may not fully capture the nuances of discussions due to the inherent brevity of tweets and the demographic skew of the platform’s user base, which may not represent the entire patient population.
  • Therapeutic Complexity: The wide therapeutic use of the studied drugs complicates the isolation of disorder-specific discourse, as many medications discussed are used for multiple conditions, not just SRD and BD.
  • Language Limitation: Only English and Spanish tweets were examined, which limits the cultural and linguistic breadth of the findings and may not fully represent global perspectives on mental health treatment.
  • Unsupervised Analysis Challenges: The unsupervised nature of the analysis may not definitively ascertain the specific medical context of discussions, suggesting a potential area for further research using supervised techniques to enhance specificity.

Why use Twitter/X & social media to understand psychiatric medications & patients?

The rationale behind researching patient experiences and perceptions regarding the pharmacological treatment of severe mental disorders through social media, specifically Twitter, is multifaceted and rooted in the unique advantages that social media analysis offers to mental health research.

1. Bridging the Gap in Patient-Provider Communication

Traditional methods of gathering patient feedback, such as clinical interviews and surveys, often fail to capture the full spectrum of patient experiences and attitudes towards medication due to factors like social desirability bias and limited interaction time.

Patients may not always feel comfortable discussing their concerns or negative experiences openly with healthcare providers.

Social media provides a candid, unfiltered platform where patients freely express their views, experiences, and challenges related to their treatments, offering invaluable insights into patient perspectives that might not be accessible through conventional research methods.

2. Real-Time, Large-Scale Data Analysis

Social media platforms generate vast amounts of data daily, presenting a unique opportunity to analyze patient discussions in real-time and at a large scale.

This allows researchers to identify trends, concerns, and changing attitudes towards treatments as they happen, providing a dynamic view of patient experiences over time.

The ability to analyze data from a 15-year span, as in this study, offers longitudinal insights into how public perceptions and discussions about mental health treatments have evolved.

3. Understanding Non-Adherence Factors

Non-adherence to pharmacological treatments is a significant challenge in managing severe mental disorders, leading to relapse, hospitalization, and decreased quality of life.

By exploring the discussions on social media, researchers can identify specific factors contributing to non-adherence, such as concerns about side effects, the stigma associated with certain medications, or issues related to access and affordability.

Understanding these factors is crucial for developing strategies to improve adherence and patient outcomes.

4. Cultural & Linguistic Insights into Treatment Perceptions

The study’s focus on both English and Spanish tweets allows for the exploration of cultural and linguistic differences in how mental health treatments are perceived and discussed.

Cultural beliefs, stigma, and access to healthcare resources can vary significantly between different linguistic and cultural communities, influencing attitudes towards medication and treatment.

Analyzing social media discussions can highlight these differences, guiding culturally sensitive approaches to treatment and patient support.

5. Informing Patient-Centered Care and Policy

The insights gained from social media analysis can inform patient-centered care by highlighting areas where patients feel their needs and concerns are not being met.

This can guide healthcare providers in tailoring their communication and support strategies to address these concerns more effectively.

Additionally, understanding public perceptions and concerns can inform healthcare policy and advocacy efforts, aiming to address systemic barriers to effective treatment and support for people with severe mental disorders.

Takeaway: Social Media vs. Antipsychotics & Mood Stabilizers

This study represents a significant advancement in understanding the complexities of patient experiences and perceptions regarding the pharmacological treatment of severe mental disorders through the lens of social media.

By analyzing a vast dataset of tweets over a 15-year period, it has illuminated the linguistic and cultural nuances that shape discussions around mental health treatments.

The findings reveal how different medications are perceived across English and Spanish tweets, highlighting concerns about drug shortages, economic and legal issues, and the emotional impact of treatment accessibility.

Such insights are invaluable for healthcare providers, offering a deeper understanding of the factors influencing treatment adherence and patient satisfaction.

Importantly, this study underscores the potential of social media as a powerful tool for real-time, large-scale research into patient perspectives, providing a complementary approach to traditional patient feedback methods.

Ultimately, leveraging social media listening can guide more patient-centered care strategies and inform policies to address the barriers and concerns facing individuals with severe mental disorders.


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