| 1. |
- Berglind, Tor, et al.
(författare)
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Levels of Hate in Online Environments
- 2019
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Ingår i: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019). - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450368681 ; , s. 842-847
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Konferensbidrag (refereegranskat)abstract
- Hate speech in online environments is a severe problem for many reasons. The space for reasoning and argumentation shrinks, individuals refrain from expressing their opinions, and polarization of views increases. Hate speech contributes to a climate where threats and even violence are increasingly regarded as acceptable. The amount and the intensity of hate expressions vary greatly between different digital environments. To analyze the level of hate in a given online environment, to study the development over time and to compare the level of hate within online environments we have developed the notion of a hate level. The hate level encapsulates the level of hate in a given digital environment. We present methods to automatically determine the hate level, utilizing transfer learning on pre-trained language models with annotated data to create automated hate detectors. We evaluate our approaches on a set of websites and discussion forums.
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| 2. |
- Holmstrom, Jesper, et al.
(författare)
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Do we Read what we Share? Analyzing the Click Dynamic of News Articles Shared on Twitter
- 2019
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Ingår i: PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019). - New York, NY, USA : Institute of Electrical and Electronics Engineers (IEEE). - 9781450368681 ; , s. 420-425
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Konferensbidrag (refereegranskat)abstract
- News and information spread over social media can have big impact on thoughts, beliefs, and opinions. It is therefore important to understand the sharing dynamics on these forums. However, most studies trying to capture these dynamics rely only on Twitters open APIs to measure how frequently articles are shared/retweeted, and therefore do not capture how many users actually read the articles linked in these tweets. To address this problem, in this paper, we first develop a novel measurement methodology, which combines the Twitter steaming API, the Bitly API, and careful sample rate selection to simultaneously collect and analyze the timeline of both the number of retweets and clicks generated by news article links. Second, we present a temporal analysis of the news cycle based on five-day-long traces (containing both clicks and retweet over time) for the news article links discovered during a seven-day period. Among other things, our analysis highlights differences in the relative timelines observed for clicks and retweets (e.g., retweet data often lags and underestimates the bias towards reading popular links/articles), and helps answer important questions regarding differences in how age-based biases and churn affect how frequently news articles shared on Twitter are accessed over time.
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