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Using Twitter to investigate opinions about multiple sclerosis treatments: a descriptive, exploratory study.

Ramagopalan S, Wasiak R, Cox AP - F1000Res (2014)

Bottom Line: These differences in sentiment scores between treatments were statistically significant (P<0.001).Many tweets about MS treatments have a non-neutral sentiment.The analysis of social media appears to be a potential avenue for exploring patient opinion about MS treatments.

View Article: PubMed Central - PubMed

Affiliation: Evidera, London, W6 8DL, UK.

ABSTRACT

Background: Multiple sclerosis (MS) is a common complex disorder, with new treatment options emerging each year. Social media is being increasingly used to investigate opinions about drugs, diseases and procedures. In this descriptive exploratory study, we sought to investigate opinions about currently available MS treatments.

Methods: The Twitter resource Topsy was searched for tweets mentioning the following MS treatments: Aubagio, Avonex, Betaferon or Betaseron, Copaxone, Extavia, Gilenya, Lemtrada, Novantrone, Rebif, Tysabri and Tecfidera between 1 Jan 2006 to 31 Jul 2014. Tweets were normalised and sentiment analysis performed.

Results: In total, there were 60037 unique tweets mentioning an MS treatment. About half of the tweets contained non-neutral sentiment. Mean sentiment scores were different for treatments ranging from -0.191to 0.282 when investigating all tweets. These differences in sentiment scores between treatments were statistically significant (P<0.001). Sentiment scores tended to be higher for oral MS treatments than injectable treatments.

Conclusions: Many tweets about MS treatments have a non-neutral sentiment. The analysis of social media appears to be a potential avenue for exploring patient opinion about MS treatments.

No MeSH data available.


Related in: MedlinePlus

Word cloud for all normalised tweets for Avonex.
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f5: Word cloud for all normalised tweets for Avonex.

Mentions: Most common words in tweets for treatments were investigated. Example word clouds for the 50 most common words (excluding commonly used English words and drug names) in all normalised tweets for Avonex, Rebif and Tysabri are shown inFigure 5,Figure 6 andFigure 7. Of note is the frequency of ‘flu’ and ‘injection’ in Avonex and Rebif tweets and ‘infusion’ and ‘pml’ in Tysabri tweets.


Using Twitter to investigate opinions about multiple sclerosis treatments: a descriptive, exploratory study.

Ramagopalan S, Wasiak R, Cox AP - F1000Res (2014)

Word cloud for all normalised tweets for Avonex.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4264640&req=5

f5: Word cloud for all normalised tweets for Avonex.
Mentions: Most common words in tweets for treatments were investigated. Example word clouds for the 50 most common words (excluding commonly used English words and drug names) in all normalised tweets for Avonex, Rebif and Tysabri are shown inFigure 5,Figure 6 andFigure 7. Of note is the frequency of ‘flu’ and ‘injection’ in Avonex and Rebif tweets and ‘infusion’ and ‘pml’ in Tysabri tweets.

Bottom Line: These differences in sentiment scores between treatments were statistically significant (P<0.001).Many tweets about MS treatments have a non-neutral sentiment.The analysis of social media appears to be a potential avenue for exploring patient opinion about MS treatments.

View Article: PubMed Central - PubMed

Affiliation: Evidera, London, W6 8DL, UK.

ABSTRACT

Background: Multiple sclerosis (MS) is a common complex disorder, with new treatment options emerging each year. Social media is being increasingly used to investigate opinions about drugs, diseases and procedures. In this descriptive exploratory study, we sought to investigate opinions about currently available MS treatments.

Methods: The Twitter resource Topsy was searched for tweets mentioning the following MS treatments: Aubagio, Avonex, Betaferon or Betaseron, Copaxone, Extavia, Gilenya, Lemtrada, Novantrone, Rebif, Tysabri and Tecfidera between 1 Jan 2006 to 31 Jul 2014. Tweets were normalised and sentiment analysis performed.

Results: In total, there were 60037 unique tweets mentioning an MS treatment. About half of the tweets contained non-neutral sentiment. Mean sentiment scores were different for treatments ranging from -0.191to 0.282 when investigating all tweets. These differences in sentiment scores between treatments were statistically significant (P<0.001). Sentiment scores tended to be higher for oral MS treatments than injectable treatments.

Conclusions: Many tweets about MS treatments have a non-neutral sentiment. The analysis of social media appears to be a potential avenue for exploring patient opinion about MS treatments.

No MeSH data available.


Related in: MedlinePlus