More Like this

1. Conspiracy and debunking narratives about COVID-19 origins on Chinese social media: How it started and who is to blame

   https://doi.org/10.37016/mr-2020-50  

   Chen, Kaiping; Chen, Anfan; Zhang, Jingwen; Meng, Jingbo; Shen, Cuihua (December 2020, Harvard Kennedy School Misinformation Review)

   null (Ed.)

   This paper studies conspiracy and debunking narratives about the origins of COVID-19 on a major Chinese social media platform, Weibo, from January to April 2020. Popular conspiracies about COVID-19 on Weibo, including that the virus is human-synthesized or a bioweapon, differ substan-tially from those in the United States. They attribute more responsibility to the United States than to China, especially following Sino-U.S. confrontations. Compared to conspiracy posts, debunking posts are associated with lower user participation but higher mobilization. Debunking narratives can be more engaging when they come from women and influencers and cite scientists. Our find-ings suggest that conspiracy narratives can carry highly cultural and political orientations. Correc-tion efforts should consider political motives and identify important stakeholders to reconstruct international dialogues toward intercultural understanding. 

   more » « less
2. Mixer: efficiently understanding and retrieving visual content at web-scale

   https://doi.org/10.14778/3476311.3476371  

   Qin, An; Xiao, Mengbai; Wu, Yongwei; Huang, Xinjie; Zhang, Xiaodong (July 2021, Proceedings of the VLDB Endowment)

   Visual contents, including images and videos, are dominant on the Internet today. The conventional search engine is mainly designed for textual documents, which must be extended to process and manage increasingly high volumes of visual data objects. In this paper, we present Mixer, an effective system to identify and analyze visual contents and to extract their features for data retrievals, aiming at addressing two critical issues: (1) efficiently and timely understanding visual contents, (2) retrieving them at high precision and recall rates without impairing the performance. In Mixer, the visual objects are categorized into different classes, each of which has representative visual features. Subsystems for model production and model execution are developed. Two retrieval layers are designed and implemented for images and videos, respectively. In this way, we are able to perform aggregation retrievals of the two types in efficient ways. The experiments with Baidu's production workloads and systems show that Mixer halves the model production time and raises the feature production throughput by 9.14x. Mixer also achieves the precision and recall of video retrievals at 95% and 97%, respectively. Mixer has been in its daily operations, which makes the search engine highly scalable for visual contents at a low cost. Having observed productivity improvement of upper-level applications in the search engine, we believe our system framework would generally benefit other data processing applications. 

   more » « less
3. Mixer: efficiently understanding and retrieving visual content at web-scale

   An Qin, Mengbai Xiao (August 2021, Proceedings of the VLDB Endowment)

   null (Ed.)

   Visual contents, including images and videos, are dominant on the Internet today. The conventional search engine is mainly designed for textual documents, which must be extended to process and manage increasingly high volumes of visual data objects.In this paper, we present Mixer, an effective system to identify and analyze visual contents and to extract their features for data retrievals, aiming at addressing two critical issues: (1) efficiently and timely understanding visual contents, (2) retrieving them at high precision and recall rates without impairing the performance. In Mixer, the visual objects are categorized into different classes, each of which has representative visual features. Subsystems for model production and model execution are developed. Two retrieval layers are designed and implemented for images and videos, respectively.In this way, we are able to perform aggregation retrievals of the two types in efficient ways. The experiments with Baidu’s production workloads and systems show that Mixer halves the model production time and raises the feature production throughput by 9.14x.Mixer also achieves the precision and recall of video retrievals at 95% and 97%, respectively. Mixer has been in its daily operations, which makes the search engine highly scalable for visual contents at a low cost. Having observed productivity improvement of upper-level applications in the search engine, we believe our system framework would generally benefit other data processing applications, 

   more » « less
4. How “Loco” Is the LOCO Corpus? Annotating the Language of Conspiracy Theories

   Mompelat, Ludovic; Tian, Zuoyu; Kessler, Amanda; Luettgen, Matthew; Rajanala, Aaryana; Kübler, Sandra; Seelig, Michelle (June 2022, Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022)

   European Language Resources Association (Ed.)

   Conspiracy theories have found a new channel on the internet and spread by bringing together like-minded people, thus functioning as an echo chamber. The new 88-million word corpus Language of Conspiracy (LOCO) was created with the intention to provide a text collection to study how the language of conspiracy differs from mainstream language. We use this corpus to develop a robust annotation scheme that will allow us to distinguish between documents containing conspiracy language and documents that do not contain any conspiracy content or that propagate conspiracy theories via misinformation (which we explicitly disregard in our work). We find that focusing on indicators of a belief in a conspiracy combined with textual cues of conspiracy language allows us to reach a substantial agreement (based on Fleiss’ kappa and Krippendorff’s alpha). We also find that the automatic retrieval methods used to collect the corpus work well in finding mainstream documents, but include some documents in the conspiracy category that would not belong there based on our definition. 

   more » « less
5. How ``Loco'' Is the LOCO Corpus? Annotating the Language of Conspiracy Theories

   Mompelat, Ludovic; Tian, Zuoyu; Kessler, Amanda; Luettgen, Matthew; Rajanala, Aaryana; Kübler, Sandra; Seelig, Michelle (July 2022, Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022)

   Conspiracy theories have found a new channel on the internet and spread by bringing together like-minded people, thus functioning as an echo chamber. The new 88-million word corpus \textit{Language of Conspiracy} (LOCO) was created with the intention to provide a text collection to study how the language of conspiracy differs from mainstream language. We use this corpus to develop a robust annotation scheme that will allow us to distinguish between documents containing conspiracy language and documents that do not contain any conspiracy content or that propagate conspiracy theories via misinformation (which we explicitly disregard in our work). We find that focusing on indicators of a belief in a conspiracy combined with textual cues of conspiracy language allows us to reach a substantial agreement (based on Fleiss{'} kappa and Krippendorff{'}s alpha). We also find that the automatic retrieval methods used to collect the corpus work well in finding mainstream documents, but include some documents in the conspiracy category that would not belong there based on our definition. 

   more » « less