OpenCrowd: A Human-AI Collaborative Approach for Finding Social Influencers via Open-Ended Answers Aggregation

Arous, Ines; Yang, Jie; Khayati, Mourad; Cudré-Mauroux, Philippe

doi:10.1145/3366423.3380254

Cited by 24 publications

(14 citation statements)

References 38 publications

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“…These indices regularly focus on requirements related to requirements that can very easily be expressed in figures (e.g. number of followers, engagement rate with the community, number of mentions and the ratio of the number of comments/likes to the number of followers) (Lou and Yuan, 2019; Arous et al , 2020). For example, the social influencer index by Aggrawal et al (2018) considers engagement, reach, sentiment and growth.…”

Section: Discussionmentioning

confidence: 99%

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Attractiveness, trustworthiness and expertise – social influencers’ winning formula?

Wiedmann

Mettenheim

2020

JPBM

220

118

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Purpose The importance of influencer marketing is constantly growing. However, little empirical research has examined influencers’ success requirements. This study aims to fill this gap by exploring whether the requirements of influencers’ attractiveness, expertise and trustworthiness are relevant for online influencer campaigns. An entry-level luxury fashion brand is the focus of the experiment. Design/methodology/approach A total of 288 participants completed an online survey evaluating the profiles of influencers who varied in terms of the three abovementioned requirements. The impacts of these requirements on brand image, brand satisfaction and brand trust as well as purchase intention and price premium were tested via structural equation modeling. Findings The results show that the most important requirement is trustworthiness, followed by attractiveness; surprisingly, the relevance of expertise is virtually nil. Research limitations/implications To date, practitioners are still struggling with the success requirements of influencer marketing. They have focused on traditional advertising models and numeric requirements such as the amount of followers. However, regarding merely these requirements can result in wrong decisions. Considering the two requirements, attractiveness and trustworthiness, in a stronger way can provide a remedy to this struggle. In future research, the relevance of the requirements in different involvement conditions and for non-attractiveness-related products might be investigated. Originality/value To the best of the authors’ knowledge, this study is one of the first to explore the success requirements that are directly related to influencers (e.g. attractiveness) rather than numeric requirements of their profiles (e.g. page rank) and the impacts of those requirements on brand image, brand satisfaction and brand trust as well as purchase intention and price premium. It adapts the Source-Credibility Model for influencers and shows that its requirements interact in a unique way that is counterintuitive and different from other endorser types such as celebrities or salespersons.

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Section: Discussionmentioning

confidence: 99%

“…Arora et al ’s (2019) index uses 39 requirements stemming from the categories Overall Footprint, Engagements and Outreach, Hourly Engagement Velocity, Daily Engagement, Velocity Audience Sentiment and Posting Rate. However, this set of requirements has been found to be often insufficient (Arous et al , 2020).…”

Section: Discussionmentioning

confidence: 99%

Attractiveness, trustworthiness and expertise – social influencers’ winning formula?

Wiedmann

Mettenheim

2020

JPBM

220

118

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“…AI is, however, a novice when it comes to collaborating with humans [37]. The term 'human-AI collaboration' has emerged in recent work studying user interaction with AI systems [7,24,118,151]. This marks both a shift to a collaborative from an automated perspective of AI, and the advancement of AI capabilities to be a collaborative partner in some domains.…”

Section: Discussionmentioning

confidence: 99%

Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems

Rezwana¹,

Maher

2023

ACM Trans. Comput.-Hum. Interact.

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Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process. Therefore the interaction model is a critical and essential component for effective co-creative systems. There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems. The primary focus of co-creativity research has been on the abilities of the AI. This paper focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems. Researchers can use COFI for modeling interaction in co-creative systems by exploring alternatives in this design space of interaction. COFI can also be beneficial while investigating and interpreting the interaction design of existing co-creative systems. We coded a dataset of existing 92 co-creative systems using COFI and analyzed the data to show how COFI provides a basis to categorize the interaction models of existing co-creative systems. We identify opportunities to shift the focus of interaction models in co-creativity to enable more communication between the user and AI leading to human-AI partnerships.

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“…In the domain of communication, symbol-based text representation is employed to mining the content in communication. Jenders et al [167] took hashtags, mentions, and sentiments as symbol features to predict viral tweets. Sheshadri and Singh [41] utilized n-gram features to analyze the news framing and explore its public and legislative impact, while Green et al [52] adopted similar features to represent tweets sent by political elites and further analyzed the polarization in elite communication on the COVID-19 pandemic.…”

Section: A11 Symbol-based Representationmentioning

confidence: 99%

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science

et al. 2021

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Computational Social Science (CSS), aiming at utilizing computational methods to address social science problems, is a recent emerging and fast-developing field. The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks, which contain rich text and network data for investigation. However, these large-scale and multi-modal data also present researchers with a great challenge: how to represent data effectively to mine the meanings we want in CSS?To explore the answer, we give a thorough review of data representations in CSS for both text and network.Specifically, we summarize existing representations into two schemes, namely symbol-based and embeddingbased representations, and introduce a series of typical methods for each scheme. Afterwards, we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS. From the statistics of these applications, we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade. Finally, we discuss several key challenges and open issues for future directions. This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers.

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OpenCrowd: A Human-AI Collaborative Approach for Finding Social Influencers via Open-Ended Answers Aggregation

Cited by 24 publications

References 38 publications

Attractiveness, trustworthiness and expertise – social influencers’ winning formula?

Attractiveness, trustworthiness and expertise – social influencers’ winning formula?

Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science

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