Multiple Social Media Platforms Research Articles

Effect of visualization of production process on consumers’ purchase intentions in farmer-assisted livestreaming

PurposeThis study investigates whether the visualization of agricultural products influences consumers’ purchase intentions in the context of farmer-assisted livestreaming in China. Moreover, it explores the moderating effect of packaging functionality and the mediating effect of consumer trust.Design/methodology/approachConsumers in China from multiple social media platforms participated in this survey, which yielded 333 valid responses for analysis.FindingsThe results revealed a positive relationship between the video presentation about the agricultural production process and consumers’ purchase intention, which is mediated by consumers’ trust. Meanwhile, packaging functionality moderates the relationship between agricultural product visualization and consumers’ purchase intentions as well as the indirect effect of consumers’ trust.Originality/valueThis study extends the application of the stimulus-organism-response (SOR) model to the field of farmer-assisted livestreaming. By building a more detailed model, this study adds to knowledge on the influencing mechanisms of consumers’ purchase intentions in farmer-assisted livestreaming.

Persistent interaction patterns across social media platforms and over time.

Growing concern surrounds the impact of social media platforms on public discourse1-4 and their influence on social dynamics5-9, especially in the context of toxicity10-12. Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years-from Usenet to contemporary social media-our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contrasting sentiments among users significantly contribute to more intense and hostile discussions. Moreover, the persistence of these patterns across three decades, despite changes in platforms and societal norms, underscores the pivotal role of human behaviour in shaping online discourse.

Using social networking evidence to examine the impact of environmental factors on social Followings: An innovative Machine learning method

Social media platforms have changed the way of communication. They are making the world more connected and provide information more quickly, which has resulted in beneficial and harmful effects on society. Analyzing data from multiple social media platforms to spot patterns and trends in user behavior in response to environmental changes is part of using social networking evidence to investigate the effects of environmental variables.The infinite collection of personal data generates privacy issues and ethical questions about data security and utilization.We present a novel framework called meta-heuristic atom search optimized dynamic artificial neural network (MASO-DANN) method for examining the impact of environmental factors using social networking evidence and the complementary abilities between MASO as well as DANN. The study aims to determine the connections between the dynamics of users' social relationships and involvement by examining the data from various online social platforms. We collected a Twitter dataset and preprocessed the data using the stop word removal preprocessing method.With the use of MASO-DANN, extensive social networking data analysis has been rendered achievable, which offers an exhaustive overview of the complex dynamics influencing online communities. The result findings examined various metrics, such as accuracy (95.07%), precision (89%), recall (88%), F1-score (90%) and RMSE (1.6).These metrics demonstrate the robustness along with the dependability of MASO-DANN and its capacity to provide more accurate predictions that evaluate the effects of environmental factors based on social networking evidence.

The more we post, the better? A comparative analysis of fan engagement on social media profiles of football leagues

PurposeIn a dynamic, continuously evolving sports landscape, social media have become an indispensable tool for sports organizations to cultivate meaningful connections with fans. The rapid pace of technological advancements has elevated these digital platforms from a supplementary role to a pivotal position within strategic management frameworks. The existing literature explores how football clubs can utilize social media, but analyzing social media strategies within the context of football leagues is lacking. The absence of comparative studies benchmarking clubs across different geographical regions while simultaneously analyzing multiple social media platforms is especially noteworthy. In this study, a comprehensive analysis of social media engagement is undertaken within esteemed football leagues spanning Europe, South America and North America.Design/methodology/approachDrawing on relationship marketing and employing content analysis as a methodological tool, the study examined 10,772 posts from the official accounts of eight football leagues on Facebook, Twitter and Instagram.FindingsAcross the leagues, the findings reveal that content quality drives engagement more than frequency. In addition, several format combinations were identified that facilitate engagement and Instagram emerged as the top social media platform for generating fan engagement.Originality/valueThis is one of the first empirical studies focusing on optimizing the use of social media to amplify fan engagement across various geographies and social media accounts and formats simultaneously.

Content Modeling in Multi-Platform Multilingual Social Media Data

An increase in the use of social media as the primary news source for the general population has created an ecosystem in which organic conversation commingles with inorganically seeded and amplified narratives, which can include public relations and marketing activity but also covert and malign influence operations. An efficient and easily understandable analysis of such data is important, as it allows relevant stakeholders to protect online communities and free discussion while better identifying activity and content that may violate social media platform terms of service. To accomplish this, we propose a method of large-scale social media data analysis, which allows for multilingual conversations to be analyzed in depth across any number of social media platforms simultaneously. Our method uses a text embedding model, i.e., a natural language processing model that holds semantic and contextual understandings of language. The model uses an “understanding” of language to represent posts as coordinates in a high-dimensional space, such that posts with similar meanings are assigned coordinates close together. We then cluster and analyze the posts to identify online topics of conversation existing across multiple social media platforms. We explicitly show how our method can be applied to four different datasets, three consisting of Chinese social media posts related to the Belt and Road Initiative and one relating to the Russia-Ukraine war, and we find politically-influenced conversations that contain misleading information relating to the Chinese government and the Russia-Ukraine war.

Assembling a multi-platform ensemble social bot detector with applications to US 2020 elections

Bots have been in the spotlight for many social media studies, for they have been observed to be participating in the manipulation of information and opinions on social media. These studies analyzed the activity and influence of bots in a variety of contexts: elections, protests, health communication and so forth. Prior to this analyzes is the identification of bot accounts to segregate the class of social media users. In this work, we propose an ensemble method for bot detection, designing a multi-platform bot detection architecture to handle several problems along the bot detection pipeline: incomplete data input, minimal feature engineering, optimized classifiers for each data field, and also eliminate the need for a threshold value for classification determination. With these design decisions, we generalize our bot detection framework across Twitter, Reddit and Instagram. We also perform feature importance analysis, observing that the entropy of names and number of interactions (retweets/shares) are important factors in bot determination. Finally, we apply our multi-platform bot detector to the US 2020 presidential elections to identify and analyze bot activity across multiple social media platforms, showcasing the difference in online discourse of bots from different platforms.

An updated review: women's concerns following miscarriage on multiple social media platforms

This study explored concerns and unsolicited advice relating to miscarriage shared on multiple social media platforms for mentions of questions, advocacy, medical advice, conflicts of interest, and terminology used to describe miscarriage. Public social media posts in English from January 1st, 2019 to June 30th, 2021 were searched using keywords related to miscarriage. A dataset of questions, advice, conflicts of interest, and terminology used was created from eligible posts determined by inclusionary and exclusionary criteria. The dataset was analyzed to identify themes and calculate statistics. Approximately 1000 posts were reviewed and 149 posts were found eligible for analysis. Of the total, 116 posts included advice and a subset of 71 posts offered questions. A total of 152 mentions of advice were identified from the total eligible posts with 82.9% of advice related to advocacy for destigmatizing miscarriage, enhancing community support, or expressing grief. 17.1% of posts offered medical advice eligible for evaluation of which 73% were accurate across all platforms. From the dataset, 103 questions were analyzed for common themes. The four most common themes of questions included grief, self-blame, quality of post-miscarriage counseling in different care settings, and inadequate medical counseling. This study indicates that women who experience miscarriage have many unanswered questions and powerful feelings relating to grief and self-blame that could have long-term impacts. Social media has become a place for women to self-advocate and connect with others for support, from which clinicians could increase their understanding of women’s unmet needs.

A Survey-Based Study of Public Awareness About COVID-19 Infection, Severity, Reinfection, Vaccine Acceptance, and Government Measures in Pakistan

Background: This study aims to assess public views and awareness about COVID-19 infection, severity, reinfection, vaccine acceptance, and government measures in Pakistan. Methods: A survey-based study was conducted between August 2021 and August 2022. The study used a self-administered questionnaire designed and distributed online using Google Forms, multiple social media platforms, and phone calls to collect data from the target population. We collected 1426 responses from male and female participants. The study was conducted in major cities throughout Pakistan, including Lahore, Multan, Faisalabad, Islamabad, Sargodha, Abbottabad, Swat, Quetta, and Mardan. Results: Most respondents (76.8%) deemed COVID-19 a serious disease, while the other participants considered it either a non-severe condition or were unsure. In this survey, 78% of respondents believed in COVID-19 immunization, 14.4% were skeptical, and 7.6% did not believe in it. Most of the study population (91.1%) had been immunized against COVID-19, and 92.4% expressed a willingness to be immunized if the vaccine was readily available. However, the remainder were either unsure or refused to receive vaccinations. 19.9% of study participants were naturally infected with COVID-19, 12% were uncertain about their previous infection, and 68.1% were not infected. COVID-19 reinfection was reported by 10.2% of study participants. The second infection was deemed more severe by 15.9% of participants than the first. It was found that half of the surveyed population used natural and traditional methods to protect themselves from COVID-19 disease. Most respondents (73%) believed that the government should make vaccinations mandatory and that people should be forced to do so. During the COVID-19 outbreak, 55.4% of people were satisfied with government initiatives, while 19.4% were dissatisfied. Conclusions: The results of the present study conducted in major cities throughout Pakistan concluded that most people trusted the COVID-19 vaccine and showed high confidence and acceptance of vaccination. Additionally, most respondents were satisfied with the government's efforts to prevent the spread of COVID-19.

Impact of ByteDance crisis communication strategies on different social media users

The impact of corporate crisis communication strategies on users’ attitudes across different platforms has emerged as a new focal point in crisis management. This study is rooted in Social Media Crisis Communication (SMCC) and employs the case of Trump’s sanctions on TikTok to analyze variations in the effects of ByteDance’s crisis communication strategies on different social media platforms. We initally identified five announcements that generated significant discussions on Toutiao and Weibo and collected the corresponding user comments (a total of 50,702). Subsequently, we utilized two approaches, machine learning and deep learning, to conduct sentiment classification tests on the text to identify the best-performing model. This model was then applied on the entire dataset for sentiment classification, followed by semantic network analysis based on the sentiment classification results. The results demostrated that the pre-trained ERNIE model outperformed the other tested models (F1 = 82.40%). Following the fourth crisis communication event, users on Toutiao and Weibo exhibited contrasting sentimental tendencies. Theoretically, we observed that users on different social media platforms relying on distinct information sources, expressed different sentimental responses to the same crisis. Social media users have a tendency to anthropomorphize corporate personality traits. In practical terms, we recommend that companies engage in crisis communication on multiple social media platforms and do not overlook the most influential platforms in the market.

Perception Analysis of Indian TV Reality Shows: Perspective of Viewers

Newspapers, popular magazines, radio, and television are examples of mass media that influence and reach enormous audiences. Television is a significant and powerful medium for mass communication. People are facilitated by television's moving pictures, which also demand attention and ultimately impact people's attitudes and actions. Audiences today prefer watching live reality shows to watching dramatic, emotionally charged television. Research of the perception analysis of Indian TV Reality programmes The Perspective of the Viewers is offered in order to comprehend the common opinions of the audiences regarding the impact of Reality TV shows on Society and their viewership. Non-fiction and fiction are the There are two primary categories of television material, each of which has multiple subcategories. Reality programmes are presented as entertainment content in the non-fiction genre, as opposed to enacted fictional events. The road to fame and fortune may be made easier by taking part in TV shows. Via Television Rating Points (TRP) and support across multiple social media platforms, controversies can reach a big audience. Channels can use viewer’s emotions in both beneficial and harmful ways. Many viewers do not understand the producers intent. The authenticity of reality TV shows has been a topic of discussion. Hence, in the current study, an effort has been made to understand how viewers view the programming elements employed in TV shows. A study was conducted in India among people between the ages of 16 and 50. As viewers frequently engage in them, get more enjoyment from the content, and enjoy them, it proves that reality television shows have a substantial impact on society.

Internet-Based Recruitment and Retention of Young Adults With Type 1 Diabetes: Cross-Sectional Study.

Multiple research strategies are required to recruit and engage a representative cohort of young adults in diabetes research. In this report, we describe an approach for internet-based recruitment for a repeated-measures descriptive study. The objective of this cross-sectional study was to determine whether internet-based recruitment through multiple social media platforms, a clinical research platform, and cooperation with community partnerships-College Diabetes Network and Beyond Type 1-would serve as an effective way to recruit a representative sample of young adults aged 18-25 years with type 1 diabetes (T1D). We conducted a repeated-measures descriptive study. We captured enrollment rates and participant characteristics acquired from each social media platform through survey data and Facebook analytics. This study was advertised via paid postings across a combination of different social media platforms (eg, Facebook, Instagram, Twitter, and Reddit). We used quarterly application postings, quarterly newsletters, and participation in the ResearchMatch registry to identify potentially eligible participants from February 3, 2021, to June 6, 2022. ResearchMatch proved to be the most cost-effective strategy overall, yielding the highest gender and racial diversity compared to other internet platforms (eg, Facebook, Instagram, Twitter, and Reddit), application postings (eg, Beyond Type 1), and newsletters (eg, College Diabetes Network and a local area college). However, we propose that the combination of these approaches yielded a larger, more diverse sample compared to any individual strategy. Our recruitment cost was US $16.69 per eligible participant, with a 1.27% conversion rate and a 30% eligibility rate. Recruiting young adults with T1D across multiple internet-based platforms was an effective strategy to yield a moderately diverse sample. Leveraging various recruitment strategies is necessary to produce a representative sample of young adults with T1D. As the internet becomes a larger forum for study recruitment, participants from underrepresented backgrounds may continue engaging in research through advertisements on the internet and other internet-based recruitment platforms.

The consumption pattern and perception of using artificial sweeteners among the public in Tabuk region, Saudi Arabia.

Obesity and weight gain have become major problems worldwide. Thus, several forms of alternative intense sweeteners are extensively used, offering a non-caloric sweet taste. To the best of our knowledge, no research has studied either the consumption pattern or the perception of using artificial sweeteners in Saudi Arabia. Our research aimed to study the usage pattern of such artificial sweeteners in the Tabuk region and estimate the knowledge of and attitudes toward their usage among the population. A cross-sectional study promoted on multiple social media platforms and face-to-face interviews in different malls and hospitals in the Tabuk region. We grouped the participants into two major groups: the users and the non-users of artificial sweeteners. Each group has been subdivided into a healthy subgroup and those with a medical record subgroup. Participants' characteristics and their choice of sweeteners were analyzed using bivariate analysis. The age, gender, and education level of the participants were adjusted using binary logistic regression in order to adjust for potential confounders. A total of 2,760 participants were included in our study. We found that more than 59% of the participants that were over 45 years old were non-hospitalized non-hospitalized diseased irrespective of their usage of artificial sweeteners. Furthermore, females, graduates, diabetics were significantly high irrespective of their subgroup. Moreover, Steviana® is the most commonly used artificial sweetener. In addition, healthy participants showed a greater perception of the usage and adverse effects of artificial sweeteners. Furthermore, bivariate analysis using logistic regression revealed significant associations (p < 0.05) with confounders such as gender, age, and education level. Educational programs and nutritional advice for the safe consumption and the daily permissible doses of artificial sweeteners are essential and should be directed specifically at females.

Sentiment Analysis of Flagship Smartphones on Social Media Using Python TextBlob And Naive Bayes Algorithm

Social media plays a crucial role in the advancement of organizations, industries, and businesses nowadays. Almost everyone is connected to social media. Each individual can interact and exchange knowledge due to the fusion of technology and social relationships. Sentiment analysis is a technique that allows extracting information from users expressing emotions, perspectives, and opinions on the internet. One strategic sector for implementing sentiment analysis is the technology sector, especially the smartphone industry. The wide range of smartphone variants available today poses a problem for individuals in finding the best smartphone product. The sentiment analysis of flagship smartphones conducted in this article aims to find the best solution between two flagship smartphones from renowned manufacturers, namely the Samsung S22 Ultra and the Xiaomi 12 Pro. The data is collected from various social media platforms such as Twitter, YouTube, and GSMArena. The collected data is then analyzed using Python TextBlob, and the analysis results in negative, positive, and neutral sentiments displayed through various visualizations. The final outcome is the assessment of Net Brand Reputation, which evaluates the reputation of a brand across multiple social media platforms.

Perceptions on Oral Ulcers From Facebook Page Categories: Observational Study.

Oral ulcers are a common condition affecting a considerable proportion of the population, and they are often associated with trauma and stress. They are very painful, and interfere with eating. As they are usually considered an annoyance, people may turn to social media for potential management options. Facebook is one of the most commonly accessed social media platforms and is the primary source of news information, including health information, for a significant percentage of American adults. Given the increasing importance of social media as a source of health information, potential remedies, and prevention strategies, it is essential to understand the type and quality of information available on Facebook regarding oral ulcers. The goal of our study was to evaluate information on recurrent oral ulcers that can be accessed via the most popular social media network-Facebook. We performed a keyword search of Facebook pages on 2 consecutive days in March 2022, using duplicate, newly created accounts, and then anonymized all posts. The collected pages were filtered, using predefined criteria to include only English-language pages wherein oral ulcer information was posted by the general public and to exclude pages created by professional dentists, associated professionals, organizations, and academic researchers. The selected pages were then screened for page origin and Facebook categories. Our initial keyword search yielded 517 pages; interestingly however, only 112 (22%) of pages had information relevant to oral ulcers, and 405 (78%) had irrelevant information, with ulcers being mentioned in relation to other parts of the human body. Excluding professional pages and pages without relevant posts resulted in 30 pages, of which 9 (30%) were categorized as "health/beauty" pages or as "product/service" pages, 3 (10%) were categorized as "medical & health" pages, and 5 (17%) were categorized as "community" pages. Majority of the pages (22/30, 73%) originated from 6 countries; most originated from the United States (7 pages), followed by India (6 pages). There was little information on oral ulcer prevention, long-term treatment, and complications. Facebook, in oral ulcer information dissemination, appears to be primarily used as an adjunct to business enterprises for marketing or for enhancing access to a product. Consequently, it was unsurprising that there was little information on oral ulcer prevention, long-term treatment, and complications. Although we made efforts to identify and select Facebook pages related to oral ulcers, we did not manually verify the authenticity or accuracy of the pages included in our analysis, potentially limiting the reliability of our findings or resulting in bias toward specific products or services. Although this work forms something of a pilot project, we plan to expand the project to encompass text mining for content analysis and include multiple social media platforms in the future.

Generalizability of Machine Learning to Categorize Various Mental Illness Using Social Media Activity Patterns

Mental illness has recently become a global health issue, causing significant suffering in people’s lives and having a negative impact on productivity. In this study, we analyzed the generalization capacity of machine learning to classify various mental illnesses across multiple social media platforms (Twitter and Reddit). Language samples were gathered from Reddit and Twitter postings in discussion forums devoted to various forms of mental illness (anxiety, autism, schizophrenia, depression, bipolar disorder, and BPD). Following this process, information from 606,208 posts (Reddit) created by a total of 248,537 people and from 23,102,773 tweets was used for the analysis. We initially trained and tested machine learning models (CNN and Word2vec) using labeled Twitter datasets, and then we utilized the dataset from Reddit to assess the effectiveness of our trained models and vice versa. According to the experimental findings, the suggested method successfully classified mental illness in social media texts even when training datasets did not include keywords or when unrelated datasets were utilized for testing.

Maximising Social Media Platforms to Enhance Sexuality Education in Rural Schools

Sexuality education is central to the South African government's strategy to promote adolescents' sexual and reproductive health, reduce student pregnancy, and prevent sexually transmitted infections, including HIV. The school-based sexuality education program aims to reduce young people’s vulnerability, enhance their decision-making skills, and improve their self-efficacy. The rising incidence of sexually transmitted infections and student pregnancy in some rural schools in King Cetshwayo district may indicate that their sexuality education programs are ineffective as a result of multiple contextual factors. In light of this, the author conducted a study that explored students' lived experiences with the sexuality education programs available in their schools. This phenomenological study was conducted in 2020. Nine schools were chosen, and 84 purposively sampled secondary school students participated in focus group interviews. The collected data were analysed using an interpretative phenomenological analysis framework. Study findings revealed that social media platforms could be utilised to improve the effectiveness of school-based sexuality education in the research setting. The study identified untapped opportunities to spread sexuality education messages to all stakeholders, including students, teachers, parents, and various community actors. Furthermore, the findings showed that using social media platforms could increase the spread and accessibility of sexuality education messages outside of the four walls of classrooms. In addition, it emerged that social media could be used to create platforms for confidential, private, and non-judgemental engagements on sensitive aspects of sexuality education. Based on the findings of the study, the author recommends that policymakers initiate and implement policies that will integrate the use of multiple social media platforms to advance the messages of sexuality education to all stakeholders. In addition, such platforms could be used for capacity building and support for both teachers and students, as well as a medium for multi-stakeholder engagement.

Optimizing user profile matching: a text-based approach

The rapid expansion of social media platforms has made linking user profiles across various networks an essential aspect of maintaining a consistent identity. With 4.66 billion users reported to be in the Websphere, many are active on multiple social media platforms simultaneously. Identifying users across multiple platforms poses challenges in integrating user profiles from various sources. Different matching schemes have been suggested over the years based on different user profile features, but very little information has been uncovered about user-generated text as a unique attribute for user profile matching, which generally poses real challenges in real-world scenarios. As many users have insufficient text and the use of non-discrete text information makes the comparison operation between the two social networks of quadratic complexity. Our study examines the different existing literature schemes for matching user profile pairs based only on their generated textual content. We suggest and evaluate the effectiveness of a two stage matching approach based on Locality Sensitive Hashing clustering and nearest neighbor search. We also present other matching results of different user representations language models and matching schemes.

Contribution of Social Media Networks Usage on Performance of Selected Upmarket Hotels Based in Kigali- Rwanda

The contribution of travel and tourism industry to Rwanda’s GDP increased from 4.7% in 2000 to 15.1% in 2019 with an average annual rate of 7.02%. It is evident that the hospitality industry relies on the use of information and communication technologies in marketing, promotional activities, sales and creation of relationships with their customers. However, there has been limited research to assess the contribution of social media networks to the performance of hospitality industry in Rwanda. Thus, the objective of the study was to assess the effect of social media network usage, determine how the selected hotels use social media networks to reach out to their customers and establish the most popular social media platforms. Using a descriptive research design, the study adopted a hybrid approach of both qualitative and quantitative approach for collection of data and employed both primary and secondary research methods to collect data including formulation of interview in form of a questionnaire. Results revealed that social media networks have significantly contributed to the performance of the selected upmarket hotels in Kigali and have been used for marketing purposes to attract customers. It was found that about 96.4% of the hotels’ clients use social media platforms to make bookings while the least indicate that at least 3.6% of most of respondents indicated that social media networks had a small impact to their hotel business. The most commonly used platforms include Facebook, YouTube, Instagram, WhatsApp and Twitter. In conclusion, the paper highlights the importance of social media in the upmarket hotel segment in Kigali, Rwanda, and its impact on the hospitality industry. The findings of the study indicate that social media has had a positive impact on the performance of upmarket hotels, with a high percentage of customers using social media to make bookings. The study recommends that hotels collect and keep records of their customers indicating which mode of communication they use while making bookings. These records must indicate which uses social media to access various hotel services. Also, hotels should ensure that they have appropriate staff with IT skills to operate and manage social media networks, including using them to get reviews for future improvement of their services. The hotels should continue investing in social media to attract customers and promote their brands. Hotels should ensure a presence on multiple social media platforms to reach a wider audience. Finally, hotels should continue to monitor and evaluate the impact of social media usage on their performance to identify areas for improvement and optimize their social media strategy. Keywords: Social Media Networks, Upmarket Hotels, Hospitality, Performance, Rwanda

The effects of firm-generated content on different social media platforms on viral marketing

Purpose This study aims to explore the impact of firm-generated content (FGC) on viral marketing on multiple social media platforms, and how social ties embedded in different social media platforms affect the motives for social dissemination. Design/methodology/approach Three studies were conducted to test the model. A quasi-field experiment (Study 1) supported this main effect. Studies 2 and 3 examined the underlying mechanism and enhanced the internal and external validity of the findings. Findings The findings revealed that warmth (vs competence)-oriented FGC is consistent with the communion (vs agency) mode and elicits greater social dissemination on social media embedded with strong (vs weak) ties. Practical implications This study illustrates that FGC that matches communication modes on multiple social media platforms embedded with different social ties will trigger viral marketing and being aware of this match is crucial for policymakers. Originality/value This research sheds light on the effects of FGC on viral marketing on multiple social media platforms embedded in different social ties.

The Use of Traditional, Complementary, and Integrative Medicine in Cancer: Data-Mining Study of 1 Million Web-Based Posts From Health Forums and Social Media Platforms

Patients with cancer are increasingly using forums and social media platforms to access health information and share their experiences, particularly in the use of traditional, complementary, and integrative medicine (TCIM). Despite the popularity of TCIM among patients with cancer, few related studies have used data from these web-based sources to explore the use of TCIM among patients with cancer. This study leveraged multiple forums and social media platforms to explore patients' use, interest, and perception of TCIM for cancer care. Posts (in English) related to TCIM were collected from Facebook, Twitter, Reddit, and 16 health forums from inception until February 2022. Both manual assessments and natural language processing were performed. Descriptive analyses were performed to explore the most commonly discussed TCIM modalities for each symptom and cancer type. Sentiment analyses were performed to measure the polarity of each post or comment, and themes were identified from posts with positive and negative sentiments. TCIM modalities that are emerging or recommended in the guidelines were identified a priori. Exploratory topic-modeling analyses with latent Dirichlet allocation were conducted to investigate the patients' perceptions of these modalities. Among the 1,620,755 posts available, cancer-related symptoms, such as pain (10/10, 100% cancer types), anxiety and depression (9/10, 90%), and poor sleep (9/10, 90%), were commonly discussed. Cannabis was among the most frequently discussed TCIM modalities for pain in 7 (70%) out of 10 cancer types, as well as nausea and vomiting, loss of appetite, anxiety and depression, and poor sleep. A total of 7 positive and 7 negative themes were also identified. The positive themes included TCIM, making symptoms manageable, and reducing the need for medication and their side effects. The belief that TCIM and conventional treatments were not mutually exclusive and intolerance to conventional treatment may facilitate TCIM use. Conversely, TCIM was viewed as leading to patients' refusal of conventional treatment or delays in diagnosis and treatment. Doctors' ignorance regarding TCIM and the lack of information provided about TCIM may be barriers to its use. Exploratory analyses showed that TCIM recommendations were well discussed among patients; however, these modalities were also used for many other indications. Other notable topics included concerns about the legalization of cannabis, acupressure techniques, and positive experiences of meditation. Using machine learning techniques, social media and health forums provide a valuable resource for patient-generated data regarding the pattern of use and patients' perceptions of TCIM. Such information will help clarify patients' needs and concerns and provide directions for research on integrating TCIM into cancer care. Our results also suggest that effective communication about TCIM should be achieved and that doctors should be more open-minded to actively discuss TCIM use with their patients.