Describes various forms of antisemitism and how they present on social media platforms. This is another great source for the background and/or next steps page(s) of my website as it informs the audience about different contexts in which antisemitism occurs, and the various, complex ways they may end up being displayed in less recognizable language. These antisemitism categories that may also end up in my glossary include:
- political
- economic
- religious
- racial
The study then includes super useful tables to display this information, in how to identify these categories. Then there is data of the frequency of antisemitic posts of each category on various platforms. These tables are excellent in showing my audience the true intense frequency of rising antisemitism, and how this antisemitism is found across a myriad of contexts and demographics.
Towards the end of the study is not as related to the content on my website, however it does include a type of "next step" aspect that can be related. This part of the study discusses the building/plans of an online system that will be able to recognize, regulate, and limit antisemitism on social media platforms. I'm wondering if I will include this or simply describe it briefly in the next steps area of my website.
Finally, the end of the study includes more incredible tables that are extremely useful to my audience, specifically examples of antisemitic content and how social media platforms currently are successful or not in recognizing through the online systems already in place. I think this is extremely useful to my audience as it shows the failures of social media to recognize antisemitic content accurately. I want to use this part of the source to expand on how social media platforms frequently have automatic systems that decide posts are not antisemitic when in reality, they are. I think this source brings excellent attention to this issue in the scope of rising antisemitism.
Citation:
Chandra, Mohit, and Et. Al. “‘Subverting the Jewtocracy’: Online Antisemitism Detection Using Multimodal Deep Learning.” Arxiv.org, Cornell University, 13 Apr. 2022, https://arxiv.org/pdf/2104.05947.pdf.