![]() One can identify more closely with user stories similar to one’s own and embrace their advice more easily. ![]() It may be easier to consult with other patients who can relate and better understand the situation based on personal experience. A sense of common ground can help break down barriers and enable conversation, increasing a person’s willingness to share. By overcoming space and distance, Twitter users form a community that disregards physical boundaries or immobility. The embarrassment caused by IBD and the need to confide in people with similar experiences help explain the creation of IBD-related communities on Twitter. The disease is invisible, and others might doubt that it exists. Outsiders cannot see that a person’s stomach hurts or that their bowels are scarred. ![]() According to patients with IBD, part of the embarrassment can be attributed to a lack of public awareness. As IBD is characterized by frequent bowel movements, people do not hasten to share their disease with others. They experience difficulties in adjusting to the changes it entails and consider themselves different from their peers. Patients describe IBD as an embarrassing disease that causes the immediate disruption of daily activities. As chronic bowel diseases, both Crohn disease and ulcerative colitis require day-to-day care for drug consumption and special nutrition. Symptoms include abdominal pain, diarrhea, and fatigue, and severe cases may result in hospitalization or surgical interventions. They involve prescription drugs and lifestyle-related solutions such as diets and therapies. Treatment options can only help with symptoms, and they affect each patient differently. There are no medications or surgical procedures that can cure IBD. The incidence of IBD is rapidly increasing, and it has evolved into a global disease. The 2 primary diseases identified with IBD, Crohn disease and ulcerative colitis, are usually diagnosed in young patients (in the age range of 15-30 years). ![]() IBD is a chronic inflammatory condition of the digestive system characterized by flares and remission states. Although a relatively large amount of research has been dedicated to diabetes or cancer, research on inflammatory bowel disease (IBD) is only just starting to consolidate. Regarding chronic conditions, previous research has focused on analyzing patients’ tweets and uncovering their Twitter community. In recent years, text mining and social network analysis have been used to detect mentions of health on Twitter or to track the spread of the COVID-19 pandemic and symptoms. Mining these informative conversations may shed some light on patients’ ways of life and support research on chronic conditions. Patients everywhere use social media to share health and treatment information, learn from each other’s experiences, and provide social support. Social networking sites and web-based communities have served as alternative information sources for patients in recent years. Using IBM Watson Service for entity sentiment analysis, we calculated the average sentiment of 420 lifestyle-related words that patients with IBD use when describing their daily routine. In the second stage, a classifier from the first stage was used to collect patients' tweets describing the different lifestyles patients adopt to deal with their disease. Different classification algorithms were examined and compared using 4 measures: precision, recall, F1 score, and the area under the receiver operating characteristic curve. The other aggregated tweet-level features to user-level features and classified the users themselves. One classified each tweet and deduced the user’s class from their tweet-level classification. We compared the performances of several classification algorithms within 2 classification approaches. We considered 3 types of features: the user’s behavior on Twitter, the content of the user’s tweets, and the social structure of the user’s network. In the first stage of the study, a machine learning method combining social network analysis and natural language processing was used to automatically classify users as patients or not.
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