Evaluating Twitter as a complementary data source for pharmacovigilance

Lardon, Jérémy; Bellet, Florelle; Aboukhamis, Rim; Asfari, Hadyl; Souvignet, Julien; Jaulent, Marie-Christine; Beyens, Marie-Noëlle; Lillo‐Lelouet, Agnès; Bousquet, Cédric

doi:10.1080/14740338.2018.1499724

Cited by 18 publications

(9 citation statements)

References 44 publications

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“…For instance, we considered tweets written in Spanish and from public Twitter users' timelines, and these users may be not representative of the general population or people suffering from depression [33,49,78,79]. Some studies have shown that Twitter users are often urban people with high levels of education, and they are generally younger than the general population [33,49,78,80,81]. We should also take into account that SSRIs are used in different types of depressive disorders and in other mental conditions.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…Another limitation may be the fact that Twitter users who share their personal drug intake may use words or expressions not included in the list of drug names employed in this study for streaming tweets, even though we tried to be exhaustive in the list of names used. Twitter texts are informal and limited by the number of characters, and they commonly include abbreviations, errors, or slang language [33,45]. All these issues can make it difficult to automatically extract drug mentions and link them to a formal lexicon [28,30,50,53,55].…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

“…For this reason, these platforms represent an important source of health information that is faster and more broadly available than other sources of health information, being unsolicited, spontaneous, and up to date. Infodemiology approaches have been developed and applied to better understand the dynamics of these platforms when used as a health information source [31][32][33]. In this context, social media users share health-related information, such as experiences with prescribed drugs [34], cancer patients' sentiments [35], opinions on vaccines [36], or online conversations on epidemic outbreaks [37].…”

Section: Introductionmentioning

confidence: 99%

“…Twitter is a very popular microblogging platform with more than 330 million active users worldwide [44]. Tweets, freely available in almost 90% of users' accounts, provide a huge amount of data that can collected in real time [28,30,33,[45][46][47][48]. Twitter users post short messages about facts, feelings, and opinions, including about health conditions [49].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study (Preprint)

Leis¹,

Ronzano²,

Mayer³

et al. 2020

Preprint

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BACKGROUND Depression is one of the most common mental disorders, being a leading cause of disability worldwide and selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed antidepressants. Some people share information about their experiences with prescribed antidepressants in social media platforms such as Twitter. The analysis of the messages posted by Twitter users under SSRI treatment can yield useful information on how these antidepressants affect users’ behavior. OBJECTIVE This study aims to compare the behavioral and linguistic characteristics of the tweets posted while users were taking SSRIs, in comparison to the tweets posted by the same users when they were not taking this medication. METHODS In the first step, the timelines of Twitter users mentioning SSRIs in their tweets were selected using a list of 128 generic and brand names of SSRIs. In the second step, two datasets of tweets were created, the in-treatment dataset (made up of the tweets posted throughout the 30 days after mentioning one SSRI) and the unknown-treatment dataset (made up of tweets posted more than 90 days before and more than 90 days after any tweet mentioning one SSRI). For each user, the changes in behavioral and linguistic features between the tweets classified in these two datasets were analyzed. 186 users and their timelines with 668,842 tweets were finally included in the study. RESULTS The number of tweets generated per day by the users when they were in-treatment was higher than when they were in the unknown-treatment period (P = .001). When the users were in-treatment, the mean percentage of tweets posted during the day-time (from 8:00 am to midnight) increased with respect to unknown-treatment periods (P = .002). The number of characters and words per tweet was higher when the users were in-treatment (P = .03 and P = .02 respectively). Regarding linguistic features, the percentages of the first and second-person singular pronouns were higher when users were in treatment (P = .008 and P = .004, respectively). CONCLUSIONS The detection of behavioral and linguistic changes when users with depression are taking medication, can provide interesting insights for monitoring the evolution of this disease as well as offering additional information related to the treatment adherence. This information may be especially useful in patients who are receiving long-term treatments such as people suffering from depression.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Section: Limitations and Future Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study (Preprint)

Leis¹,

Ronzano²,

Mayer³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This body of science spans a number of disparate areas, including tracking the spread of influenza [ 3 , 4 ], oral health problems [ 5 ], sleep issues [ 6 ], obesity [ 7 ], cardiovascular disease [ 8 ], diabetes [ 9 ], mental health [ 10 ], and health care enrollment [ 11 ]. In addition, there is burgeoning interest in the use of innovative and nontraditional methods (such as mining and analyzing social media data) as a means to better surveil the opioid epidemic, with Twitter becoming a complementary data source for pharmacovigilance [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

Anwar¹,

Khoury²,

Aldridge³

et al. 2020

JMIR Public Health Surveill

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Background Over the last two decades, deaths associated with opioids have escalated in number and geographic spread, impacting more and more individuals, families, and communities. Reflecting on the shifting nature of the opioid overdose crisis, Dasgupta, Beletsky, and Ciccarone offer a triphasic framework to explain that opioid overdose deaths (OODs) shifted from prescription opioids for pain (beginning in 2000), to heroin (2010 to 2015), and then to synthetic opioids (beginning in 2013). Given the rapidly shifting nature of OODs, timelier surveillance data are critical to inform strategies that combat the opioid crisis. Using easily accessible and near real-time social media data to improve public health surveillance efforts related to the opioid crisis is a promising area of research. Objective This study explored the potential of using Twitter data to monitor the opioid epidemic. Specifically, this study investigated the extent to which the content of opioid-related tweets corresponds with the triphasic nature of the opioid crisis and correlates with OODs in North Carolina between 2009 and 2017. Methods Opioid-related Twitter posts were obtained using Crimson Hexagon, and were classified as relating to prescription opioids, heroin, and synthetic opioids using natural language processing. This process resulted in a corpus of 100,777 posts consisting of tweets, retweets, mentions, and replies. Using a random sample of 10,000 posts from the corpus, we identified opioid-related terms by analyzing word frequency for each year. OODs were obtained from the Multiple Cause of Death database from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Least squares regression and Granger tests compared patterns of opioid-related posts with OODs. Results The pattern of tweets related to prescription opioids, heroin, and synthetic opioids resembled the triphasic nature of OODs. For prescription opioids, tweet counts and OODs were statistically unrelated. Tweets mentioning heroin and synthetic opioids were significantly associated with heroin OODs and synthetic OODs in the same year (P=.01 and P<.001, respectively), as well as in the following year (P=.03 and P=.01, respectively). Moreover, heroin tweets in a given year predicted heroin deaths better than lagged heroin OODs alone (P=.03). Conclusions Findings support using Twitter data as a timely indicator of opioid overdose mortality, especially for heroin.

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Assessment of Word Embedding Techniques for Identification of Personal Experience Tweets Pertaining to Medication Uses

Jiang

Feng

Calix

et al. 2019

Precision Health and Medicine

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Evaluating Twitter as a complementary data source for pharmacovigilance

Abstract: With the use of dedicated tools, Twitter could become a complementary source of information for pharmacovigilance, despite a major limitation regarding causality assessment of ADRs in individual tweets, which may improve with the new limitation to 280 characters per tweet.

Cited by 18 publications

References 44 publications

Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study (Preprint)

Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study (Preprint)

Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

Assessment of Word Embedding Techniques for Identification of Personal Experience Tweets Pertaining to Medication Uses

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