Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at, first published .
Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter

Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter

Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter

Original Paper

1Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, Madrid, Spain

2Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcala, Alcalá de Henares, Spain

3Department of Endocrinology and Clinical Nutrition, Hospital Universitario Infanta Leonor, Madrid, Spain

4Department of Preventive Medicine and Public Health, School of Medicine, University of Navarra, Pamplona, Spain

5Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcala, Madrid, Spain

6Ramón y Cajal Institute of Sanitary Research, Madrid, Spain

7Pharmacology Unit, Biomedical Sciences Department, University of Alcalá, Madrid, Spain

8Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain

9Psychiatry Service, Hospital Universitario Principe de Asturias, Alcalá de Henares, Spain

10Department of Legal Medicine and Psychiatry, Complutense University, Madrid, Spain

11Internal Medicine and Autoimmunity/Rheumatology Service, Hospital Universitario Principe de Asturias, Madrid, Spain

12Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas, Madrid, Spain

Corresponding Author:

Miguel Angel Alvarez-Mon, MD, PhD

Department of Psychiatry and Mental Health

Hospital Universitario Infanta Leonor

Av Gran Vía del Este 80

Madrid, 28031


Phone: 34 911 91 80 00


Background: Antiobesity drugs are prescribed for the treatment of obesity in conjunction with healthy eating, physical activity, and behavior modification. However, poor adherence rates have been reported. Attitudes or beliefs toward medications are important to ascertain because they may be associated with patient behavior. The analysis of tweets has become a tool for health research.

Objective: The aim of this study is to investigate the content and key metrics of tweets referring to antiobesity drugs.

Methods: In this observational quantitative and qualitative study, we focused on tweets containing hashtags related to antiobesity drugs between September 20, 2019, and October 31, 2019. Tweets were first classified according to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. We additionally rated it as positive or negative. Furthermore, we classified any links included within a tweet as either scientific or nonscientific. Finally, the number of retweets generated as well as the dissemination and sentiment score obtained by the antiobesity drugs analyzed were also measured.

Results: We analyzed a total of 2045 tweets, 945 of which were excluded according to the criteria of the study. Finally, 320 out of the 1,100 remaining tweets were also excluded because their content, although related to drugs for obesity treatment, did not address the efficacy, side effects, or adherence to medication. Liraglutide and semaglutide accumulated the majority of tweets (682/780, 87.4%). Notably, the content that generated the highest frequency of tweets was related to treatment efficacy, with liraglutide-, semaglutide-, and lorcaserin-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to liraglutide and semaglutide. Semaglutide-related tweets obtained the highest probability of likes and were the most disseminated within the Twitter community.

Conclusions: This analysis of posted tweets related to antiobesity drugs shows that the interest, beliefs, and experiences regarding these pharmacological treatments are heterogeneous. The efficacy of the treatment accounts for the majority of interest among Twitter users.

J Med Internet Res 2021;23(10):e24336



Obesity is an increasingly prevalent disease, with high rates of associated morbidity and mortality [1]. The treatment of obesity remains only partially effective [2]. Moreover, the pharmacological treatment of obesity is becoming a more significant tool in the management of the disease [2]. However, frequency in the use of antiobesity drug treatments among those patients that could potentially benefit from them is minimal [3]. Moreover, both personal and social attitudes, combined with accessible information on available treatments, have been shown to be relevant for obtaining the expected clinical outcomes of pharmacological intervention [4]. Furthermore, it has been shown that social support is a potential beneficial component of weight loss programs [5,6].

In recent years, social media has become a pivotal instrument for disseminating knowledge [7]. Accordingly, the internet has modified how people communicate and how they share and seek out information regarding health [8]. Social networks are extensively used for the study of obesity, including the analysis of public attitudes, the social support of patients, patients’ behavior, and treatment efficacy [9-11]. Being that information pertaining to health posted over social media is oftentimes more spontaneous in nature, it serves more as a complementary perspective to data collected from medical surveys, clinical trials, and consultancies with medical professionals [12-14].

Twitter, one of the most popular and widely used social media platforms, is currently considered to be an effective channel of communication [15]. Within this context, different agents in the health sector have realized Twitter´s potential for acquiring and distributing medical information [16,17]. In addition, Twitter users demonstrate a great interest in obesity and eating disorders [18,19]. Moreover, it has been shown that Twitter can be an effective platform for delivering interventions aimed at treating obesity [20]. The analysis of tweets about obesity, diet, and treatments is a recent relevant area of study for understanding the actual sentiments of society, patients, and health providers [21]. The trivialization, stigmatization, and mockery directed at obesity and other disorders by Twitter users have been reported [22]. Until now, however, topics of interest among Twitter users regarding the pharmacological treatment of obesity have not been identified. Furthermore, the dissemination of medication-related tweets tied to obesity remains unknown. The analysis of the feelings and experiences toward pharmacological treatment is relevant for the understanding of patients’ attitudes to these drugs and the identification of concerns and needs potentially related to treatment adherence [23,24].

In this study, we performed an analysis of the content and key metrics of all the tweets generated concerning medications approved by the US Food and Drug Administration (FDA) for obesity treatment over a period of 6 weeks. We also investigated the areas of interest of those tweets containing medical content and the inclusion of links to related informative resources. Finally, we investigated the tweets’ dissemination and overall sentiment.

Data Collection

In this observational quantitative and qualitative study, we focused on searching for tweets that referred to medications approved by the FDA for the treatment of overweight or obese status: Xenical, orlistat, Alli, Belviq, lorcaserin, Qsymia, phentermine-topiramate, Contrave, bupropion-naltrexone, Saxenda, Victoza, liraglutide, Ozempic, and semaglutide. The inclusion criteria for tweets were the following: being public; using any of the previously mentioned hashtags; being posted between September 20, 2019, and October 31, 2019; and being posted in English language. This 6-week period was chosen to avoid any potential bias within the content of the tweets. In addition, we obtained the number of likes each tweet generated, the date and time of each tweet, and the potential reach and impact of each hashtag.

Search Tool

We used the Twitter Firehose data stream, which is managed by Gnip and allows access to 100% of all public tweets that match a certain criteria (query) [25]. In our study, the search criteria were the previously mentioned hashtags. Tweet Binder, the search engine we employed, uses node.js and PHP language, which enabled us to analyze tweets in JavaScript Object Notation (JSON) format (used by Gnip).

Content Analysis Process

All 2045 retrieved tweets were directly inspected by 2 raters (MAAM and MLV). First, we scanned all of the tweets, excluding 945 tweets that provided information that was too limited (eg, tweets consisting mainly of hashtags), that contained only pictures, or that included hashtags of more than 1 treatment. All the remaining tweets were considered for thematic content analysis. Second, we created a codebook based on our research questions, our previous experience in analyzing tweets, and what we determined to be the most common tweet themes. Third, 2 raters analyzed 150 tweets separately to test the suitability of the codebook. Discrepancies were discussed between the raters and with another author (MAM). After the codebook was revised, the interrater reliability was reassessed with a different set of 150 tweets. As this resulted in adequate κ values (range 0.68-0.99), the raters then proceeded to analyze 1100 tweets equally distributed among both. Each tweet, depending on its content, was categorized under side effects, efficacy, or adherence. In addition, users were classified into 3 categories: patients and relatives, health professionals, or health institutions. These categorizations were defined based on the description of user profiles and the content of user tweets. In those cases in which the nature of the user was not possible to know, they were classified as indeterminate. Finally, we analyzed any links included within a tweet, classifying them as either scientific or nonscientific. More specifically, those links attributed to a scientific source, including medical journals, academic institutions, hospitals, and official websites, were classified as scientific. The classification criteria we used and examples of tweets are shown in Textbox 1.

Examples of tweets related to efficacy, side effects, and adherence (usernames and personal names have been removed).

Efficacy (the ability or inability of a treatment to provide a beneficial effect)

  • “Oral semaglutide can effectively and safely reduce blood glucose, body weight and systolic blood pressure: A systematic review and meta-analysis.”
  • “Ozempic is superior to Invokana in reducing A1c and body weight”.
  • “More than just #weightloss ? Liraglutide improves hepatic steatosis and metabolic dysfunctions in a 3-week dietary mouse model of nonalcoholic steatohepatitis.”
  • “The PIONEER 4 trial showed that oral semaglutide is noninferior to injectable liraglutide and superior to placebo in improving glycemic control and weight loss at 26 weeks among patients with type 2 #diabetes.”.

Side effects (any effect that is secondary to the one intended either adverse or beneficial; tweets discussing tolerability of the drug were also included)

  • “No side effects with the Ozempic and I’ve been on it since July of last year. Now the metformin is a whole different story. First week or so the sight of food made me sick and it made my stomach act up. I’m actually glad to be off of it.”
  • “Does Contrave Make Anyone Else's Hands Shake?”
  • “I saw the Saxenda results on people I know. It is fantastic. However, it comes with its challenges. Nausea, headaches and terrible moods. glucagon-like peptide 1 (GLP-1) analogs, such as liraglutide, is the possibility of developing pancreatitis. #usmle”
  • “A bit personal, but I'm curious to hear others' stories. I've been taking Ozempic for a year or so, but the last 6-8 weeks I've started throwing up quite a bit. I don't have a history of this at all. Anyone else had this problem with Ozempic? Thank you!”
  • “Those embalming leaves seem to have less side effects than ozempic.”
  • “Gallstone Disease in Patients Treated with Liraglutide: In a large randomized trial a small but statistically significant rise in this adverse event was noted.”

Adherence (the degree of conformity to the recommendations about the treatment with respect to the timing, dosage, or frequency)

  • “I'm a quibbler, I can't help it. There is a medicine called Qsymia that seems effective for long term weight loss. Its not without side effects or risks.”
  • “Morning Twitter nightmare week over. Week 21 liraglutide. Despite everything total adherence. Adherence to duloxetine not good. Quorn burger for breakfast & fruit. Happy today.”
Textbox 1. Examples of tweets related to efficacy, side effects, and adherence (usernames and personal names have been removed).

Measuring Interest and Influence on Twitter

We analyzed the number of likes generated by each tweet as an indicator of user interest on a given topic. We also measured the potential reach and impact of all analyzed hashtags in order to best assess tendencies in the dissemination of tweets. In this study, impact was defined as a numerical value representing the potential views a tweet may receive, while reach was defined as a numerical value measuring the potential audience of the hashtag.

In addition, we measured how positive or negative a hashtag was on a scale from 1 (negative) to 100 (positive). Sentiment analysis tools were used to analyze all words contained in a tweet, with each word having its own score that could vary depending on the context. The average score of all the tweets with a certain hashtag determined that hashtag’s overall sentiment score. According to this score, we classified each hashtag into 3 categories: negative (0-40), neutral (>40-60), and positive (>60-100).

Ethical Considerations

This study was approved by the Research Ethics Committee at the University of Alcala.

Statistical Analysis

A descriptive study of the sample was performed. The qualitative variables are described as absolute frequency (number) and relative frequency (percentage). The percentages found were compared using the chi-square test. The numbers of retweets and likes per original tweet about the different hashtags were verified by graphs and statistical test (Kolmogorov-Smirnof test), and they did not follow a normal distribution. The differences found between the treatment groups were compared using the Kruskal-Wallis test. All statistical analyses were performed using SPSS version 25 (IBM Corp).

Liraglutide and Semaglutide Accumulated the Most Interest Among Twitter Users

Our search tool provided a total of 2045 tweets, 945 of which were excluded according to the criteria of the study. Next, 320 out of the 1100 remaining tweets were also excluded because their content, although related to drugs for obesity treatment, did not address the efficacy, side effects, or adherence to the medication. Finally, we classified the content of the remaining 780 tweets.

The number of tweets with hashtags referencing the 6 drug groups approved for obesity treatment were significantly different, with the incidence of tweets related to liraglutide and semaglutide at least being 10 times higher than that related to the other 5 drug groups (Table 1).

Next, we found significant differences in the distribution of the content. Notably, the content that generated the highest frequency of tweets was that related to treatment efficacy, with liraglutide-, semaglutide-, and lorcaserin-related tweets accumulating the highest proportion of positive consideration (P<.001). In contrast, the highest percentage of tweets with a negative valuation towards efficacy was found in those posts related to bupropion-naltrexone (3/30, 10%), while those containing a mention of liraglutide (14/319, 4.4%) and semaglutide (2/363, 0.6%) had a much lower negative percentage.

Tweets with a negative valuation of side effects were mainly observed in those related to orlistat (4/15, 26.7%) and bupropion-naltrexone (4/30, 13.3%) but rarely in those mentioning liraglutide, semaglutide, and phentermine-topiramate. On the other hand, tweets with a positive valuation of side effects were minimal and were found mainly in those posts related to bupropion-naltrexone, semaglutide, and liraglutide (P=.04). Finally, the frequency of tweets with content related to adherence to treatment was low, with negative considerations predominant among liraglutide, bupropion-naltrexone, and semaglutide (P<.001). On the other hand, positive valuations of adherence to treatment were observed in those tweets related to orlistat, semaglutide, and liraglutide.

Table 1. Descriptive characteristics of the original tweets included in the analysis categorized by total amount per drug and category (side effects, efficacy, adherence, and link).
CategoryOrlistat, n (%) (N=15)Lorcaserin, n (%) (N=27)Phentermine-topiramate, n (%) (N=26)Bupropion-naltrexone, n (%) (N=30)Liraglutide, n (%) (N=319)Semaglutide, n (%) (N=363)P valuea
Side effects.04

No mention11 (73.3)27 (100)25 (96.2)25 (83.3)299 (93.7)337 (92.8)

Positive0 (0)0 (0)0 (0)1 (3.3)2 (0.6)8 (2.2)

Negative4 (26.7)0 (0)1 (3.8)4 (13.3)18 (5.6)18 (5.0)

No mention9 (60.0)8 (29.6)15 (57.7)14 (46.7)62 (19.4)131 (36.1)

Positive6 (40.0)19 (70.4)11 (42.3)13 (43.3)243 (76.2)230 (63.4)

Negative0 (0)0 (0)0 (0)3 (10.0)14 (4.4)2 (0.6)

No mention14 (93.3)27 (100)26 (100)28 (93.3)288 (90.3)336 (92.6)

Positive1 (6.7)0 (0)0 (0)0 (0)3 (0.9)21 (5.8)

Negative0 (0)0 (0)0 (0)2 (6.7)28 (8.8)6 (1.7)

None6 (40.0)2 (7.4)4 (15.4)18 (60.0)93 (29.2)91 (25.1)

Scientific1 (6.7)11 (40.7)5 (19.2)0 (0)196 (61.4)270 (74.4)

Nonscientific8 (53.3)14 (51.9)17 (65.4)12 (40)30 (9.4)2 (0.6)

aChi-square tests were conducted to assess statistical differences.

Scientific Links Were Mainly Found Within Liraglutide- and Semaglutide-Related Tweets

We found significant differences between the distribution of those tweets including a link, whether scientific or nonscientific, among the 6 different drug groups (P<.001 Table 1). More specifically, we found the highest percentages of tweets with scientific links in those posts related to liraglutide (196/319, 61.4%) and semaglutide (270/363, 74.4%), followed by those tweets referencing lorcaserin (11/27, 40.7%) and phentermine-topiramate (5/26, 19.2%). The frequency of tweets with a nonscientific link was very low in those related to semaglutide and liraglutide; on the other hand, more than half of the tweets referencing phentermine-topiramate, lorcaserin, and orlistat included a nonscientific link.

As liraglutide and semaglutide accumulated the majority of tweets (682/780, 87.4%), we decided to investigate the use of links in these tweets depending on their content (Table 2). The use of links was mainly focused on those tweets with a positive consideration towards the efficacy of the treatment, whereas in those tweets referencing side effects and adherence to treatment, the use of links was marginal.

Table 2. Descriptive characteristics of the original tweets included in the analysis categorized by total amount per drug and category.
CategoryLiraglutide, n (%)P valueaSemaglutide, n (%)P valuea

Without link (N=93)With link (N=226)
Without link (N=91)With link (N=272)
Side effects.006

No mention81 (87.1)218 (96.5)
77 (84.6)260 (95.6)

Positive1 (1.1)0 (0)
2 (2.2)6 (2.2)

Negative11 (11.8)8 (3.5)
12 (13.2)6 (2.2)

No mention24 (25.8)38 (16.8)
17 (18.7)114 (41.9)

Positive55 (59.1)188 (83.2)
72 (79.1)158 (58.1)

Negative14 (15.1)0 (0)
2 (2.2)0 (0)

No mention73 (78.5)215 (95.1)
80 (87.9)256 (94.1)

Positive3 (3.2)0 (0)
5 (5.5)16 (5.9)

Negative17 (18.3)11 (4.9)
6 (6.6)0 (0)

aChi-square tests were conducted to assess statistical differences.

Semaglutide-Related Tweets Obtained the Highest Probability of Likes and Were the Most Disseminated Within the Twitter Community

We found that the probabilities of a tweet being liked between the groups were significantly different (P<.001). Semaglutide-related tweets accumulated the highest number of likes per tweet (median 3; 95% CI 1-12). In addition, we analyzed the number of likes received per tweet as classified by the inclusion or absence of a link. We found no differences in the median of likes per tweet between those posts including or not including a link (P=.27).

We found that semaglutide-related tweets had the highest potential reach and impact (2,522,621 and 4,676,763 , respectively), which was double that of liraglutide (719,382 and 1,631,062, respectively). On the other hand, both parameters were markedly lower for bupropion-naltrexone (996,398 and 1,603,556, respectively), orlistat (486,533 and 697,956 , respectively), phentermine-topiramate (183,919 and 187,094, respectively), and lorcaserin (29,420 and 30,341, respectively).

Regarding the sentiment analyses of the content of the tweets, we found that those posts related to semaglutide (mean 79.67), liraglutide (mean 61.46), lorcaserin (mean 75.14), and phentermine-topiramate (mean 60.06) received positive sentiment. However, the sentiment was neutral for orlistat (mean 43.9) and bupropion-naltrexone (mean 53.8).

Health Institutions Were the Most Active Twitter Users

We investigated the type of users that posted the tweets. Of the total number of tweets, 7.9% (62/780) were posted by users identified as patients or relatives, 16% (125/780) by health institutions, and 27.1% (211/780) by health care professionals. Of the remaining 49% (382/780) of tweets, the users were considered indeterminate.

Next, we investigated those tweets related to side effects according to the different types of users and found significant differences in the frequency and content of the postings (Table 3; P<.001). Patients were the users that posted most about the presence of side effects, whereas health institutions mentioned the presence of side effects the least. Moreover, we also found significant differences between users in regards to tweets about efficacy (P<.001) and adherence (P<.001). Interestingly, patients were also those who most frequently expressed a lack of efficacy or adherence (Table 4 and Table 5). On the other hand, users classified as health institutions were those that posted most frequently on the efficacy of treatment and promoted adherence to it. Additionally, we assessed who the users were that most frequently included a link within their tweets (Table 6). We found that health institutions included a link, either designated as scientific or nonscientific, more frequently in their posts than did users classified as health professionals or patients (P<.001). Finally, we assessed the frequency of user postings according to the different antiobesity drugs analyzed, finding significant differences among them. In particular, health institutions generated the majority of tweets concerning the latest antiobesity drugs.

Table 3. Descriptive characteristics of those tweets referencing side effects in which the user was categorized as a patient, health professional, or health institution. Tweets were further classified as either not mentioning side effects or mentioning them positively or negatively.
Side effectsPatients, n (%)Health professionals, n (%)Health institutions, n (%)Total, n (%)
No mention40 (11.59)114 (33.04)191 (55.36)345 (100)
Positive2 (66.67)0 (0)1 (33.33)3 (100)
Negative11 (39.29)8 (28.57)9 (32.14)28 (100)
Total53 (14.10)122 (32.45)201 (53.46)376 (100)
Table 4. Descriptive characteristics of those tweets referencing efficacy in which the user was categorized as a patient, health professional, or health institution. Tweets were further classified as either not mentioning efficacy or mentioning it positively or negatively.
EfficacyPatients, n (%)Health professionals, n (%)Health institutions, n (%)Total, n (%)
No mention27 (20.45)34 (25.76)71 (53.79)132 (100)
Positive19 (8.19)83 (35.78130 (56.03)232 (100)
Negative7 (58.33)5 (41.67)0 (0)12 (100)
Total53 (14.10)122 (32.45)201 (53.46)376 (100)
Table 5. Descriptive characteristics of those tweets referencing adherence in which the user was categorized as a patient, health professional, or health institution. Tweets were further classified as either not mentioning adherence or mentioning it positively or negatively.
AdherencePatients, n (%)Health professionals, n (%)Health institutions, n (%)Total, n (%)
No mention37 (10.60)117 (33.52)195 (55.87)349 (100)
Positive1 (12.5)1 (12.5)6 (75)8 (100)
Negative15 (78.95)4 (21.05)0 (0)19 (100)
Total53 (14.10)122 (32.45)201 (53.46)376 (100)
Table 6. Descriptive characteristics of those tweets in which the user was categorized as a patient, health professional, or health institutions, and further designated as either not including a link or including a scientific or a nonscientific link.
LinkPatients, n (%)Health professionals, n (%)Health institutions, n (%)Total, n (%)
None52 (50)39 (37.50)13 (12.50)104 (100)
Scientific0 (0)70 (31.25)154 (68.75)224 (100)
Nonscientific1 (2.1)13 (27.08)34 (70.83)48 (100)
Total53 (14.10)122 (32.45)201 (53.46)376 (100)

Principal Findings

In this study, we have found that Twitter users show an interest in antiobesity drugs and mainly focus on semaglutide and liraglutide. These tweets are centered on the efficacy of the treatment and principally refer to liraglutide, semaglutide, and lorcaserin. Tweet content containing a negative consideration of side effects was mainly observed in those tweets related to orlistat and bupropion-naltrexone. The frequency of tweets with content related to adherence to treatment was marginal. The highest percentages of tweets with scientific links were observed in those related to liraglutide and semaglutide. Furthermore, those tweets referencing semaglutide obtained the highest potential reach and impact.

Diet, exercise, and lifestyle are considered relevant elements for maintaining a weight within the recommended range [26]. The prevention and treatment of overweight and obese status are considered public health priorities [27]. Currently, the use of pharmacological treatment is becoming pivotal in obesity management [28].

The outcomes of pharmacological treatments for chronic diseases are conditioned by different elements [29]. The efficacy and side effects of antiobesity drugs are critical for the success of these treatments [30]. However, the results of real-world pharmacological interventions are also dependent on treatment adherence [30]. Different factors, such as access to drug information and social considerations, modulate patients’ attitudes toward treatment [31,32]. Therefore, identifying patients’ areas of concern and the sources of information used are relevant for improving the clinical outcomes. Additionally, patients with health behaviors that are frequently disapproved of by society are oftentimes reluctant to disclose to health providers their noncompliance with treatment and medical advice [33]. In this context, the anonymity of social media may provide greater insight into the beliefs, interests, and experiences of patients with regard to antiobesity drugs. Furthermore, family members of the patients, doctors, and health care providers can also participate in the social media community and post their comments related to the pharmacological treatment of obesity. The identification of the needs, concerns, and feelings of the patients related to their treatment may improve their adherence and contribute positively to achieving therapeutic objectives [34].

Interest in Antiobesity Drugs on Twitter

Our data show that antiobesity drugs are areas of interest within the Twitter community. The attention paid to antiobesity drugs is reflected in the number of tweets posted with content related to these drugs, which was higher than that reported on other medications employed to treat chronic diseases [35,36]. Furthermore, it is also significant that the majority of the posted tweets were related to the medical aspects of antiobesity drugs in contrast to the reported results of other medications in which the interest generated was nonmedical in nature [37]. In addition, the interest of Twitter users was mainly centered on liraglutide and semaglutide, which accumulated nearly 90% of the tweets. Likewise, differences in interest shown by social media users towards drugs with similar clinical indications have been previously observed, for example, in the case of statins [38].

In addition, we also studied users’ areas of interest regarding antiobesity drugs. Our findings show that the one clearly predominant area was drug efficacy, but with different levels of positive consideration being present, as liraglutide, semaglutide, and lorcaserin achieved the highest valuations. With the exception of the tweets related to orlistat, the frequency of references to the side effects of the antiobesity was very low. Considering our study was conducted in the period from September 2019 to October 2019, it is notable that lorcaserin was soon after withdrawn from the market (February 2020) due to its potentially severe side effects [39].

There may be several reasons for these findings. First, differences in the efficacy could explain the different frequencies found between tweets posted about different drugs [30]. However, this factor is unlikely to prove fully conclusive as the results obtained in the clinical trials and metanalysis referencing these drugs do not wholly support the differences observed. Second, there were different patterns of side effects [30]. However, Twitter users have shown little interest in the side effects of the drugs, and it has only been a focus in relation to orlistat and bupropion-naltrexone. Thus, the references to side effects do not seem to explain the differences in interest. Third, it is possible that Twitter users might show a special interest in treatments suppressing appetite. However, this mechanism of action is not only characteristic of glucagon-like peptide-1 inhibitors but also lorcaserin, which only obtained a small number of tweets. The fourth potential factor is availability of scientific information. Liraglutide and semaglutide, the latest approved drugs, have been subject to most of the recent clinical trials involving antiobesity drugs. Furthermore, both drugs have the majority of tweets containing scientific links with content focused mainly on efficacy. Thus, it is possible that recent scientific publications and reports on clinical trials involving liraglutide and semaglutide might in part explain the significant interest toward these drugs within the Twitter community. The fifth reason may involve accessible information in the press and social media. It is known that newly launched drugs or recently approved indications receive enhanced interest from pharmaceutical companies, health providers, and the media [40]. Consequently, the fact that liraglutide and semaglutide have been the most recently approved drugs might explain the special interest towards them.

The dissemination of tweets referencing the 6 different drugs was also heterogenous, with the potential reach and impact of semaglutide clearly being the highest, demonstrating total numbers similar to those of the other 5 drugs combined. This finding could be explained by the fact that semaglutide has been the most recently approved antiobesity drug. In addition, clinical trials concerning semaglutide have been an area of interest for companies and prestigious scientific journals, which have published results in support of the approval of this treatment [41,42].

Furthermore, our data show that most of the tweets were focused on efficacy and rarely mention side effects or discuss issues related to personal adherence to treatment. Thus, there might have been a bias in the information related to these drugs. Moreover, in contrast to previous reports, most of the tweets included a link to sources supporting their content [43]. Interestingly, concerns about the efficacy or tolerability of antiobesity drugs were identified mostly in those tweets not containing a link. The sharing of personal experiences is unlikely to be associated with a link. The analysis of these tweets reveals relevant information for health care providers because many patients that question the efficacy of treatments or abandon their treatments entirely tend to withhold this information from doctors due to feelings of shame or guilt [33]. Indeed, social media has been found to identify side effects not always uncovered via traditional surveys [44]. In semaglutide- and liraglutide-related tweets, most links were scientific, whereas with the rest of the drugs, the majority of links contained a nonscientific source. This may indicate that tweets discussing issues related to semaglutide or liraglutide may be based on medical articles reporting on efficacy. This may thus imply that pharmaceutical companies, scientific journals, and researchers play a key role in Twitter conversations related to obesity medications. Therefore, it is possible that a potential increase in the investigation of the adherence to obesity treatment might increase the dissemination and relevance given by social media users to this pivotal aspect of obesity management.

The important role of social media in generating popular opinion and emotions via information distribution has been established [45], and social media has become a pivotal instrument for sharing knowledge and news [46]. The interest shown by Twitter users in antiobesity drugs support the relevance of social media in the diffusion of medical information. In addition, social media is used to carry out medical interventions, promote preventive health campaigns, and recruit participants for medical research [47,48].

Finally, we studied the sentiment of tweets and found that most drugs obtained a high score. In contrast, bupropion-naltrexone obtained a very low score. This low sentiment toward bupropion-naltrexone may be reflective of the other indications this drug has: smoking cessation or depression. In this regard, it is worth noting the mockery of psychiatric conditions in Twitter [49]. However, this is unlikely to be the only cause of the low score obtained in the sentiment analysis because phentermine-topiramate is frequently prescribed for the treatment of psychiatric conditions and obtained an average score. Thus, it is possible to suggest that this low sentiment is due to the poor effect bupropion has on weight loss [30].

Understanding the public view of the pharmacological treatment of obesity is useful to better appraising the perceived demands for clinical care related to this condition. It could also help designing better promotional health initiatives and awareness strategies that include content of interest to social media users. In addition, this information can facilitate open conversations about a patient’s most common concerns during the medical consultancy. Although this study focused on antiobesity drugs, these results provide relevant information which more than likely can be applied to other pharmacological treatments. The involvement of health institutions in related conversations over social media appears to be desirable given the interest raised by the medical content posted on Twitter.

Strengths and Limitations

First, the relevance of Twitter as a marker of patient´s voice is a matter of controversy. In addition, tweets do not necessarily reflect the overall experience of patients. Second, regarding the collection of tweets, there is the risk that some were not detected since they might have used different hashtags. However, including brand names and the active pharmaceutical ingredients minimized possible bias related to the choice of hashtags. Third, we did not determine whether the date of FDA approval affected Twitter activity differently in more recent versus less recent medications. Fourth, the codebook design and text analysis involved a degree of subjectivity. Nevertheless, this methodology is consistent with previous medical research studies using Twitter. Although computerized machine learning methods have been tested to automatically identify and classify topics in medical research in social media, we used an analysis strategy based on the raters’ clinical expertise in obesity, which constitutes a qualitative advantage compared to automated strategies [50]. Finally, the inability to verify the precise identity of the majority of Twitter users posting about antiobesity drugs, in addition to a lack of geolocational data, may constitute a limitation in our capacity to interpret results.


This study demonstrates that Twitter users show an interest in antiobesity pharmacological treatment. The positive consideration of the efficacy of antiobesity drugs accounted for the majority of tweets. In contrast, the side effects of these treatments was only marginally described. Adherence to treatment received little interest from the Twitter community. The nature of the links included in the tweets was heterogenous between the different antiobesity drugs. Thus, this study highlights the opportunity for sharing scientific information, especially that aimed at promoting adherence to pharmacological treatment, which we have detected as being overlooked.


This work was partially supported by grants from the Fondo de Investigación de la Seguridad Social, Instituto de Salud Carlos III (PI18/01726), Spain; and the Programa de Actividades de I+D de la Comunidad de Madrid en Biomedicina (B2017/BMD-3804), Madrid, Spain.

Conflicts of Interest

None declared.

  1. González-Muniesa P, Mártinez-González MA, Hu F, Després JP, Matsuzawa Y, Loos R, et al. Obesity. Nat Rev Dis Primers 2017 Jun 15;3:17034. [CrossRef] [Medline]
  2. Bray GA, Frühbeck G, Ryan DH, Wilding JPH. Management of obesity. Lancet 2016 May 07;387(10031):1947-1956. [CrossRef] [Medline]
  3. Thomas CE, Mauer EA, Shukla AP, Rathi S, Aronne LJ. Low adoption of weight loss medications: A comparison of prescribing patterns of antiobesity pharmacotherapies and SGLT2s. Obesity (Silver Spring) 2016 Sep;24(9):1955-1961 [FREE Full text] [CrossRef] [Medline]
  4. Cassell B, Gyawali CP, Kushnir VM, Gott BM, Nix BD, Sayuk GS. Beliefs about GI medications and adherence to pharmacotherapy in functional GI disorder outpatients. Am J Gastroenterol 2015 Oct;110(10):1382-1387 [FREE Full text] [CrossRef] [Medline]
  5. Greaves CJ, Sheppard KE, Abraham C, Hardeman W, Roden M, Evans PH, et al. Systematic review of reviews of intervention components associated with increased effectiveness in dietary and physical activity interventions. BMC Public Health 2011;11:119 [FREE Full text] [CrossRef] [Medline]
  6. Verheijden MW, Bakx JC, van Weel C, Koelen MA, van Staveren WA. Role of social support in lifestyle-focused weight management interventions. Eur J Clin Nutr 2005 Aug;59 Suppl 1:S179-S186. [CrossRef] [Medline]
  7. McGowan BS, Wasko M, Vartabedian BS, Miller RS, Freiherr DD, Abdolrasulnia M. Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. J Med Internet Res 2012;14(5):e117 [FREE Full text] [CrossRef] [Medline]
  8. Lee K, Hoti K, Hughes J, Emmerton L. Dr Google and the consumer: a qualitative study exploring the navigational needs and online health information-seeking behaviors of consumers with chronic health conditions. J Med Internet Res 2014;16(12):e262 [FREE Full text] [CrossRef] [Medline]
  9. Waring ME, Jake-Schoffman DE, Holovatska MM, Mejia C, Williams JC, Pagoto SL. Social media and obesity in adults: a review of recent research and future directions. Curr Diab Rep 2018 Apr 18;18(6):34. [CrossRef] [Medline]
  10. Pagoto S, Schneider KL, Evans M, Waring ME, Appelhans B, Busch AM, et al. Tweeting it off: characteristics of adults who tweet about a weight loss attempt. J Am Med Inform Assoc 2014;21(6):1032-1037. [CrossRef] [Medline]
  11. Chou WS, Prestin A, Kunath S. Obesity in social media: a mixed methods analysis. Transl Behav Med 2014 Sep;4(3):314-323 [FREE Full text] [CrossRef] [Medline]
  12. Cavazos-Rehg PA, Krauss MJ, Costello SJ, Kaiser N, Cahn ES, Fitzsimmons-Craft EE, et al. "I just want to be skinny.": A content analysis of tweets expressing eating disorder symptoms. PLoS One 2019;14(1):e0207506 [FREE Full text] [CrossRef] [Medline]
  13. Golder S, Norman G, Loke YK. Systematic review on the prevalence, frequency and comparative value of adverse events data in social media. Br J Clin Pharmacol 2015 Oct;80(4):878-888. [CrossRef] [Medline]
  14. Joseph AJ, Tandon N, Yang LH, Duckworth K, Torous J, Seidman LJ, et al. #Schizophrenia: Use and misuse on Twitter. Schizophr Res 2015 Jul;165(2-3):111-115. [CrossRef] [Medline]
  15. Attai D, Cowher M, Al-Hamadani M, Schoger J, Staley A, Landercasper J. Twitter social media is an effective tool for breast cancer patient education and support: patient-reported outcomes by survey. J Med Internet Res 2015 Jul 30;17(7):e188 [FREE Full text] [CrossRef] [Medline]
  16. Thangasamy IA, Leveridge M, Davies BJ, Finelli A, Stork B, Woo HH. International Urology Journal Club via Twitter: 12-month experience. Eur Urol 2014 Jul;66(1):112-117. [CrossRef] [Medline]
  17. Nikiphorou E, Studenic P, Ammitzbøll CG, Canavan M, Jani M, Ospelt C, EMEUNET. Social media use among young rheumatologists and basic scientists: results of an international survey by the Emerging EULAR Network (EMEUNET). Ann Rheum Dis 2017 Apr;76(4):712-715. [CrossRef] [Medline]
  18. Turner-McGrievy GM, Beets MW. Tweet for health: using an online social network to examine temporal trends in weight loss-related posts. Transl Behav Med 2015 Jun;5(2):160-166 [FREE Full text] [CrossRef] [Medline]
  19. Viguria I, Alvarez-Mon MA, Llavero-Valero M, Asunsolo Del Barco A, Ortuño F, Alvarez-Mon M. Eating disorder awareness campaigns: thematic and quantitative analysis using Twitter. J Med Internet Res 2020 Jul 14;22(7):e17626 [FREE Full text] [CrossRef] [Medline]
  20. Turner-McGrievy GM, Tate DF. Weight loss social support in 140 characters or less: use of an online social network in a remotely delivered weight loss intervention. Transl Behav Med 2013 Sep;3(3):287-294 [FREE Full text] [CrossRef] [Medline]
  21. Sinnenberg L, Buttenheim A, Padrez K, Mancheno C, Ungar L, Merchant R. Twitter as a tool for health research: a systematic review. Am J Public Health 2017 Jan;107(1):e1-e8. [CrossRef] [Medline]
  22. Lydecker J, Cotter E, Palmberg A, Simpson C, Kwitowski M, White K. Does this Tweet make me look fat? A content analysis of weight stigma on Twitter. Eat Weight Disord Internet 2016:229 [FREE Full text] [CrossRef] [Medline]
  23. Mohammed MA, Moles RJ, Chen TF. Medication-related burden and patients' lived experience with medicine: a systematic review and metasynthesis of qualitative studies. BMJ Open 2016 Feb 02;6(2):e010035 [FREE Full text] [CrossRef] [Medline]
  24. Gagnon MD, Waltermaurer E, Martin A, Friedenson C, Gayle E, Hauser DL. Patient beliefs have a greater impact than barriers on medication adherence in a community health center. J Am Board Fam Med 2017;30(3):331-336 [FREE Full text] [CrossRef] [Medline]
  25. Joseph K, Landwehr P, Carley K. Two 1%s don't make a whole: comparing simultaneous samples from Twitter's streaming API. ISPRS International Journal of Geo-Information Internet 2014:75 [FREE Full text] [CrossRef]
  26. Patnode CD, Evans CV, Senger CA, Redmond N, Lin JS. behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults without known cardiovascular disease risk factors: updated evidence report and systematic review for the US preventive services task force. JAMA 2017 Jul 11;318(2):175-193. [CrossRef] [Medline]
  27. Lloyd-Jones D, Hong Y, Labarthe D, Mozaffarian D, Appel L, Van Horn L, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction. Circulation 2010 Feb 02;121(4):586-613 [FREE Full text] [CrossRef]
  28. Jensen M, Ryan D, Apovian C, Ard J, Comuzzie A, Donato K, American College of Cardiology/American Heart Association Task Force on Practice Guidelines, Obesity Society. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation 2014 Jun 24;129(25 Suppl 2):S102-S138 [FREE Full text] [CrossRef] [Medline]
  29. Middleton KMR, Patidar SM, Perri MG. The impact of extended care on the long-term maintenance of weight loss: a systematic review and meta-analysis. Obes Rev 2012 Jun;13(6):509-517. [CrossRef] [Medline]
  30. Khera R, Murad MH, Chandar AK, Dulai PS, Wang Z, Prokop LJ, et al. Association of pharmacological treatments for obesity with weight loss and adverse events: a systematic review and meta-analysis. JAMA 2016;315(22):2424-2434. [CrossRef] [Medline]
  31. Nanna MG, Navar AM, Zakroysky P, Xiang Q, Goldberg AC, Robinson J, et al. Association of patient perceptions of cardiovascular risk and beliefs on statin drugs with racial differences in statin use: insights from the patient and provider assessment of lipid management registry. JAMA Cardiol 2018 Aug 01;3(8):739-748 [FREE Full text] [CrossRef] [Medline]
  32. Bradley CK, Wang TY, Li S, Robinson JG, Roger VL, Goldberg AC, et al. Patient-reported reasons for declining or discontinuing statin therapy: insights from the PALM registry. J Am Heart Assoc 2019 Apr 02;8(7):e011765 [FREE Full text] [CrossRef] [Medline]
  33. Levy AG, Scherer AM, Zikmund-Fisher BJ, Larkin K, Barnes GD, Fagerlin A. Prevalence of and factors associated with patient nondisclosure of medically relevant information to clinicians. JAMA Netw Open 2018 Nov 02;1(7):e185293 [FREE Full text] [CrossRef] [Medline]
  34. Barbosa CD, Balp M, Kulich K, Germain N, Rofail D. A literature review to explore the link between treatment satisfaction and adherence, compliance, and persistence. Patient Prefer Adherence 2012;6:39-48 [FREE Full text] [CrossRef] [Medline]
  35. Giunti G, Claes M, Dorronzoro Zubiete E, Rivera-Romero O, Gabarron E. Analysing sentiment and topics related to multiple sclerosis on Twitter. Stud Health Technol Inform 2020 Jun 16;270:911-915. [CrossRef] [Medline]
  36. Cook N, Mullins A, Gautam R, Medi S, Prince C, Tyagi N, et al. Evaluating patient experiences in dry eye disease through social media listening research. Ophthalmol Ther 2019 Sep;8(3):407-420 [FREE Full text] [CrossRef] [Medline]
  37. Alghamdi A, Abumelha K, Allarakia J, Al-Shehri A. Conversations and misconceptions about chemotherapy in Arabic tweets: content analysis. J Med Internet Res 2020 Jul 29;22(7):e13979 [FREE Full text] [CrossRef] [Medline]
  38. Golder S, O'Connor K, Hennessy S, Gross R, Gonzalez-Hernandez G. Assessment of beliefs and attitudes about statins posted on twitter: a qualitative study. JAMA Netw Open 2020 Jun 01;3(6):e208953 [FREE Full text] [CrossRef] [Medline]
  39. Halpern B, Mancini MC. Should the same safety scrutiny of antiobesity medications be applied to other chronic usage drugs? Obesity (Silver Spring) 2020 Jul;28(7):1171-1172. [CrossRef] [Medline]
  40. Carlos S, de Irala J, Hanley M, Martínez-González. The use of expensive technologies instead of simple, sound and effective lifestyle interventions: a perpetual delusion. J Epidemiol Community Health 2014 Sep;68(9):897-904 [FREE Full text] [CrossRef] [Medline]
  41. Husain M, Birkenfeld AL, Donsmark M, Dungan K, Eliaschewitz FG, Franco DR, PIONEER 6 Investigators. Oral semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 2019 Aug 29;381(9):841-851. [CrossRef] [Medline]
  42. Marso SP, Bain SC, Consoli A, Eliaschewitz FG, Jódar E, Leiter LA, SUSTAIN-6 Investigators. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med 2016 Dec 10;375(19):1834-1844. [CrossRef] [Medline]
  43. Pereira-Sanchez V, Alvarez-Mon MA, Asunsolo Del Barco A, Alvarez-Mon M, Teo A. Exploring the extent of the Hikikomori phenomenon on Twitter: mixed methods study of Western language tweets. J Med Internet Res 2019 May 29;21(5):e14167 [FREE Full text] [CrossRef] [Medline]
  44. Kim MG, Kim J, Kim SC, Jeong J. Twitter analysis of the nonmedical use and side effects of methylphenidate: machine learning study. J Med Internet Res 2020 Feb 24;22(2):e16466 [FREE Full text] [CrossRef] [Medline]
  45. King G, Schneer B, White A. How the news media activate public expression and influence national agendas. Science 2017 Nov 10;358(6364):776-780 [FREE Full text] [CrossRef] [Medline]
  46. Alvarez-Mon M, Asunsolo Del Barco A, Lahera G, Quintero J, Ferre F, Pereira-Sanchez V, et al. Increasing interest of mass communication media and the general public in the distribution of tweets about mental disorders: observational study. J Med Internet Res 2018 May 28;20(5):e205 [FREE Full text] [CrossRef] [Medline]
  47. Teo AR, Liebow SB, Chan B, Dobscha SK, Graham AL. Reaching those at risk for psychiatric disorders and suicidal ideation: Facebook advertisements to recruit military veterans. JMIR Ment Health 2018 Jul 05;5(3):e10078 [FREE Full text] [CrossRef] [Medline]
  48. Hales SB, Davidson C, Turner-McGrievy GM. Varying social media post types differentially impacts engagement in a behavioral weight loss intervention. Transl Behav Med 2014 Dec;4(4):355-362 [FREE Full text] [CrossRef] [Medline]
  49. Alvarez-Mon M, Llavero-Valero M, Sánchez-Bayona R, Pereira-Sanchez V, Vallejo-Valdivielso M, Monserrat J, et al. Areas of interest and stigmatic attitudes of the general public in five relevant medical conditions: thematic and quantitative analysis using Twitter. J Med Internet Res 2019 May 28;21(5):e14110 [FREE Full text] [CrossRef] [Medline]
  50. Saha K, Torous J, Ernala SK, Rizuto C, Stafford A, De Choudhury M. A computational study of mental health awareness campaigns on social media. Transl Behav Med 2019 Nov 25;9(6):1197-1207 [FREE Full text] [CrossRef] [Medline]

FDA: US Food and Drug Administration
JSON: JavaScript Object Notation

Edited by R Kukafka; submitted 15.09.20; peer-reviewed by JA Giron-González, SP Nischwitz, D Valdez, M Claes; comments to author 05.10.20; revised version received 02.12.20; accepted 12.08.21; published 26.10.21


©Miguel Angel Alvarez-Mon, Maria Llavero-Valero, Angel Asunsolo del Barco, Cristina Zaragozá, Miguel A Ortega, Guillermo Lahera, Javier Quintero, Melchor Alvarez-Mon. Originally published in the Journal of Medical Internet Research (, 26.10.2021.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.