A Modular Data-Driven Architecture for Empathetic Conversational Agents

Scotti, Vincenzo; Tedesco, Roberto; Sbattella, Licia

doi:10.1109/bigcomp51126.2021.00080

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…To improve accuracy we also plan to extend our analysis by considering emoji and emoticon symbols, which are pervasive in SM content, as studied in [101,102]. Finally, we plan to design conversation agents capable of participating in some discussions [103] during online conversations, based on the learned conversational patterns, to implement nudging and hinting strategies towards the users. Such conversation agents would be beneficial for the events' organizers, to facilitate customer relationship management [104,105] and to foster behavioral changes in users.…”

Section: Discussionmentioning

confidence: 99%

Graph-Based Conversation Analysis in Social Media

Brambilla

Sabet

Kharmale

et al. 2022

BDCC

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Social media platforms offer their audience the possibility to reply to posts through comments and reactions. This allows social media users to express their ideas and opinions on shared content, thus opening virtual discussions. Most studies on social networks have focused only on user relationships or on the shared content, while ignoring the valuable information hidden in the digital conversations, in terms of structure of the discussion and relation between contents, which is essential for understanding online communication behavior. This work proposes a graph-based framework to assess the shape and structure of online conversations. The analysis was composed of two main stages: intent analysis and network generation. Users’ intention was detected using keyword-based classification, followed by the implementation of machine learning-based classification algorithms for uncategorized comments. Afterwards, human-in-the-loop was involved in improving the keyword-based classification. To extract essential information on social media communication patterns among the users, we built conversation graphs using a directed multigraph network and we show our model at work in two real-life experiments. The first experiment used data from a real social media challenge and it was able to categorize 90% of comments with 98% accuracy. The second experiment focused on COVID vaccine-related discussions in online forums and investigated the stance and sentiment to understand how the comments are affected by their parent discussion. Finally, the most popular online discussion patterns were mined and interpreted. We see that the dynamics obtained from conversation graphs are similar to traditional communication activities.

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Section: Discussionmentioning

confidence: 99%

Graph-Based Conversation Analysis in Social Media

Brambilla

Sabet

Kharmale

et al. 2022

BDCC

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“…Consequently, we plan to design predictive models as proposed in [75] to predict the popularity of online content generated by the stakeholders to maximize the visibility and popularity of their news and advertisements. Last but not least, the social media core will enable us to extract the conversation graphs [76,77] around specific topics, build conversational agents [78], and facilitate customer relationship management.…”

Section: Discussionmentioning

confidence: 99%

THOR: A Hybrid Recommender System for the Personalized Travel Experience

Sabet

Shekari

Guan

et al. 2022

BDCC

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One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected.

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“…The proposed methodology can be used by brands to improve their marketing strategies on social media. As a potential implication resulting from the proposed methodology, we propose analyzing the social conversation graphs, as designed by Brambilla et al [49,50] to build conversational agents [52,53] that potentially elevate the users' engagement.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Brand Awareness Strategies on Social Media in the Luxury Market: the Case of Italian Fashion

Brambilla¹,

Badrizadeh²,

Mohammadi³

et al. 2022

Preprint

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The rapid penetration of social media has been redefining every facet of the old marketing and customer engagement tactics, not only for the low-end and mass products but also for luxury brands. In this context, brands are dealing with the challenge of keeping the balance between using mass marketing strategies concurrent with accentuating the exclusivity of their offerings. Social media can be considered a boon if brands employ them to reach the right audience and use the right platform by incorporating the right content. In this work, we propose a sector-specific, integrated, and holistic investigation of the social media strategies of luxury brands, together with the impact they generate in terms of the engagement level of the users as an indicator of their success. We provide empirical validation of the method in the sector of luxury fashion brands in the Italian market, providing qualitative and quantitative analysis of the content shared on social media, considering the type, timing, and modality of the sharing. We evaluate consumer-brand engagement in different contexts, including important live events in the field.

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A Modular Data-Driven Architecture for Empathetic Conversational Agents

Cited by 8 publications

References 14 publications

Graph-Based Conversation Analysis in Social Media

Graph-Based Conversation Analysis in Social Media

THOR: A Hybrid Recommender System for the Personalized Travel Experience

Analyzing Brand Awareness Strategies on Social Media in the Luxury Market: the Case of Italian Fashion

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