![]() ![]() The months following the assault were the loneliest and darkest days of my life. The assault took away my worth, my privacy, my body, my confidence and my voice. My life as I knew it was stolen from me and shattered beyond recognition. A part of me died that day that I will never get back. 6 years later, I still have these nights.īefore NovemI was a different person. I went to sleep praying that I wouldn’t wake up the next morning. While my friends went out and did fun things as normal 20 something year olds would do – I would lay in my bed and cry myself to sleep until I was numb. The assault stole all of that away from me. I have always been told that your 20’s are supposed to be full of possibilities and figuring out who you really are. I had dreams and aspirations of who I could be. I had all the confidence in the world and a bright future ahead of me. Six years ago I was a 23 year old college student. When I try to think about the impact the assault has had on me, I cannot help but think too of the impact that the assault will continue to have on me for the rest of my life. It is difficult, if not impossible to summarize such a life-changing 6 years into one statement. I appreciate the chance I have been given to talk about the impact the assault has had on my life. “Your honour, I would like to thank you for the opportunity to speak today. The following text, which was read aloud in court, as been modified to protect the identity of the victim and her family. A jury found him guilty of one count of sexual assault causing bodily harm in the 2016 incident.Īs part of the sentencing hearing on Thursday, the woman assaulted by Hoggard delivered her victim impact statement. Hoggard was found guilty earlier this year of sexually assaulting an Ottawa woman. The sentencing hearing for Hedley frontman Jacob Hoggard’s sexual assault case got underway on Thursday in Toronto. Warning: This story contains graphic details that may be disturbing to the reader. Originally published in the Journal of Medical Internet Research (). ©Vasiliki Foufi, Tatsawan Timakum, Christophe Gaudet-Blavignac, Christian Lovis, Min Song. ![]() The results reported in this paper are promising and demonstrate the need for more in-depth studies on the way patients with chronic diseases express themselves on social media platforms.Ĭhronic disease data mining social media. This study showed that people are eager to share their personal experience with chronic diseases on social media platforms despite possible privacy and security issues. The manual validation of the extracted entities showed a very good performance of the system at the entity extraction task (3682/5151, 71.48% extracted entities were correctly labeled). The relation pair anatomy-disease was the most frequent (5550 occurrences), the highest frequent entities in this pair being cancer and lymph. The most highly mentioned entities were those related to oncological disease (2884 occurrences of cancer) and asthma (2180 occurrences). ![]() In total, 82,138 entities and 30,341 relation pairs were extracted from the Reddit dataset. Using PKDE4J, we extracted 2 types of entities and relations: biomedical entities and relations and subject-predicate-object entity relations. PKDE4J is a text mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. For entity and relation extraction from this corpus, we employed the PKDE4J tool developed by Song et al (2015). We collected a corpus of 17,624 text posts from disease-specific subreddits of the social news and discussion website Reddit. The major focus of our research is the study of biomedical entities found in health social media platforms and their relations and the way people suffering from chronic diseases express themselves. This paper aimed to report a study of entities related to chronic diseases and their relation in user-generated text posts. In particular, social media platforms about health provide a different insight into patient's experiences with diseases and treatment than those found in the scientific literature. Social media platforms constitute a rich data source for natural language processing tasks such as named entity recognition, relation extraction, and sentiment analysis. ![]()
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