Mixed Methods and the Future of Multi-Modal Media

Saeideh Bakhshi, David A. Shamma
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Abstract

Humans are complex and their behaviors follow complex multi-modal patterns, however to solve many social computing problems one often looks at complexity in large-scale yet single point data sources or methodologies. While single data/single method techniques, fueled by large scale data, enjoyed some success, it is not without fault. Often with one type of data and method, all the other aspects of human behavior are overlooked, discarded, or, worse, misrepresented. We identify this as two succinct problems. First, social computing problems that cannot be solved using a single data source and need intelligence from multiple modals and, second, social behavior that cannot be fully understood using only one form of methodology. Throughout this talk, we discuss these problems and their implications, illustrate examples, and propose new directives to properly approach in the social computing research in today's age.
混合方法与多模态媒体的未来
人类是复杂的,他们的行为遵循复杂的多模态模式,然而,为了解决许多社会计算问题,人们经常在大规模的单点数据源或方法中看到复杂性。虽然在大规模数据的推动下,单一数据/单一方法技术取得了一些成功,但它并非没有缺点。通常使用一种类型的数据和方法,人类行为的所有其他方面都被忽视、丢弃,或者更糟糕的是,被歪曲。我们认为这是两个简单的问题。首先,社会计算问题无法使用单一数据源解决,需要来自多个模式的智能;其次,仅使用一种形式的方法无法完全理解社会行为。在整个演讲中,我们讨论了这些问题及其影响,举例说明,并提出了在当今时代正确处理社会计算研究的新指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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