Understanding users in the era of social media is challenging, requiring organizations to adopt novel computation-aided approaches. To exemplify such an approach, we retrieved information on millions of interactions with YouTube video content from a major Middle Eastern media outlet, to automatically generate personas that capture how different audience segments interact with thousands of individual content pieces. Then, we used qualitative data to provide additional insights into the automatically generated persona profiles. Our findings provide insights into social media usage in the Middle East and demonstrate the application of a novel methodology that generates culturally adapted personas of social media audiences, summarizing complex social analytics data into human portrayals that are easy to understand by end users in real organizations.

Hateful commenting, also known as 'toxicity', frequently takes place within news stories in social media. Yet, the relationship between toxicity and news topics is poorly understood. To analyze how news topics relate to the toxicity of user comments, we classify topics of 63,886 online news videos of a large news channel using a neural network and topical tags used by journalists to label content. We score 320,246 user comments from those videos for toxicity and compare how the average toxicity of comments varies by topic. Findings show that topics like Racism, Israel-Palestine, and War & Conflict have more toxicity in the comments, and topics such as Science & Technology, Environment & Weather, and Arts & Culture have less toxic commenting. Qualitative analysis reveals five themes: Graphic videos, Humanistic stories, History and historical facts, Media as a manipulator, and Religion. We also observe cases where a typically more toxic topic becomes non-toxic and where a typically less toxic topic becomes "toxicified" when it involves sensitive elements, such as politics and religion. Findings suggest that news comment toxicity can be characterized as topic-driven toxicity that targets topics rather than as vindictive toxicity that targets users or groups. Practical implications suggest that humanistic framing of the news story (i.e., reporting stories through real everyday people) can reduce toxicity in the comments of an otherwise toxic topic.

We conduct a mixed-method study to better understand the content consumption patterns of Middle Eastern social media users and to explore new ways to present online data by using automatic persona generation. First, we analyze millions of content interactions on YouTube to dynamically generate personas describing behavioral patterns of different demographic groups. Second, we analyze interview data on social media users in the Middle Eastern region to generate additional insights into the dynamically generated personas. Our findings provide insights into social media users in the Middle East, as well as present a novel methodology of using computational analysis and qualitative data enrichment to generate descriptive and culturally receptive personas from social media audiences.

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