Visual brand presence in digital social media; first funding period: Setup and Design of Networks by Providers.

推特、Snapchat或WhatsApp等数字社交媒体(DSM)上每天分享的图片超过30亿张。尽管帝斯曼的品牌标识越来越多，但这些形象数据还没有在科学的营销研究中得到调查。应用包括社交媒体活动和跟踪与竞争相关的视觉品牌存在。在第二个资助期，应调查视觉品牌存在的营销相关性。应制定有关活动优化和绩效指标的程式化事实。A2项目是先前研究的自然延伸，之前的研究仅限于文本交流，并在音乐行业中进行了实证应用。结果表明，企业传播的特定内容类别对网络需求、网络结构和网络传播量的影响高于传播量。考虑到这一发展的同时，本研究现在正在研究更多行业的品牌相关视觉传播。机器学习算法可以实现这一点。它们能够以越来越高的精度检测物体和人脸，并且可以相对容易地训练来检测品牌标志。从经验上看，Twitter似乎是一个合适的调查媒介，因为它在公开可访问的图像传播方面已经达到了特别突出的地位。到目前为止，已经收集了270多个不同行业品牌的850万条推文。在帖子层面上，这些数据可以研究哪些因素有助于在需求侧管理中传播品牌标识(例如，标识的大小/位置、周围物体、面部、情绪、对比、相关文本)。谷歌搜索查询、网站流量和基于调查的品牌跟踪在品牌层面上可用来调查视觉品牌存在与品牌成功之间的关系。研究结果将首次为DSM中视觉品牌存在的相关性提供科学的指示，并指出DSM传播方面的关键成功因素。

More than 3 billion images are shared every day on digital social media (DSM) such as Twitter, Snapchat or WhatsApp. These image data have not been investigated in scientific marketing research despite the growing number of brand logos in DSM. Applications include social media campaigns and tracking of visual brand presence relative to competition. In the second funding period, the marketing relevance of visual brand presence shall be investigated. Stylized facts on campaign optimization and performance indicators shall be developed. Project A2 is a natural extension of the previous research, which was limited to text communication with empirical applications in the music industry. According to the results, specific content categories of firm communication impact online demand, network structure and network communication over and above communication volume. Considering the development in the meantime, this research now investigates brand-related visual communication across more industries. Machine learning algorithms are available to accomplish this. These are capable of detecting objects and faces with increasing precision and can be relatively easily trained to detect brand logos. Empirically, Twitter appears an appropriate medium of investigation, since it has reached a particular prominence with regard to publicly accessible image communication. 8.5 million Tweets on more than 270 different brands of various industries have been collected so far. On a post level, these data allow studying which factors are conducive to the dissemination of brand logos in DSM (e.g., size/ position of logo, surrounding objects, faces, sentiments, contrasts, associated texts). Google search queries, website traffic and survey based brand trackings are available at the brand level to investigate the relationship between visual brand presence and brand success across time. The results will provide a first scientific indication on the relevance of visual brand presence in DSM and point out key success factors in terms of DSM dissemination.