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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

Boxplots of sentiment scores of all normalised tweets with tweets containing share/stock information and company names excluded.
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f4: Boxplots of sentiment scores of all normalised tweets with tweets containing share/stock information and company names excluded.

Mentions: Summing sentiment scores for all tweets showed positive overall sentiment scores for all drugs apart from Novantrone (all analyses) and Tysabri (only after filtering for company names and stock/share tweet data). Gilenya had the highest summed sentiment score in all analyses. Boxplots of sentiment scores of all normalised tweets, normalised tweets excluding those that contained share/stock information and normalised tweets excluding those that contained share/stock information and company names are shown inFigure 2,Figure 3 andFigure 4. The mean sentiment score ranged from -0.191 to 0.282 (all tweet data); and -0.193 to 0.247 (after filtering for company names and stock/share tweet data). Novantrone always had the lowest mean sentiment score. Tecfidera had the highest mean score in the all tweet data, and Aubagio had the highest mean score in the filtered for company names and stock/share tweet data. The mean sentiment scores were different in all analyses (P<0.001 in the all tweet data, filtered for stock/share tweet data and filtered for company names and stock/share tweet data).


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

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

Boxplots of sentiment scores of all normalised tweets with tweets containing share/stock information and company names excluded.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Boxplots of sentiment scores of all normalised tweets with tweets containing share/stock information and company names excluded.
Mentions: Summing sentiment scores for all tweets showed positive overall sentiment scores for all drugs apart from Novantrone (all analyses) and Tysabri (only after filtering for company names and stock/share tweet data). Gilenya had the highest summed sentiment score in all analyses. Boxplots of sentiment scores of all normalised tweets, normalised tweets excluding those that contained share/stock information and normalised tweets excluding those that contained share/stock information and company names are shown inFigure 2,Figure 3 andFigure 4. The mean sentiment score ranged from -0.191 to 0.282 (all tweet data); and -0.193 to 0.247 (after filtering for company names and stock/share tweet data). Novantrone always had the lowest mean sentiment score. Tecfidera had the highest mean score in the all tweet data, and Aubagio had the highest mean score in the filtered for company names and stock/share tweet data. The mean sentiment scores were different in all analyses (P<0.001 in the all tweet data, filtered for stock/share tweet data and filtered for company names and stock/share tweet data).

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