Social media platforms like Twitter (now X) have been pivotal in information dissemination and public engagement, especially during COVID-19. A key goal for public health experts was to encourage prosocial behavior that could impact local outcomes such as masking and social distancing. Given the importance of local news and guidance during COVID-19, the objective of our research is to analyze the effect of localized engagement, on social media conversations. This study examines the impact of geographic co-location, as a proxy for localized engagement between public health experts (PHEs) and the public, on social media. We analyze a Twitter conversation dataset from January 2020 to November 2021, comprising over 19 K tweets from nearly five hundred PHEs, along with approximately 800 K replies from 350 K participants. Our findings reveal that geo-co-location is associated with higher engagement rates, especially in conversations on topics including masking, lockdowns, and education, and in conversations with academic and medical professionals. Lexical features associated with emotion and personal experiences were more common in geo-co-located contexts. This research provides insights into how geographic co-location influences social media engagement and can inform strategies to improve public health messaging.

Mobile health programs are becoming an increasingly popular medium for dissemination of health information among beneficiaries in less privileged communities. Kilkari is one of the world's largest mobile health programs which delivers time sensitive audio-messages to pregnant women and new mothers. We have been collaborating with ARMMAN, a non-profit in India which operates the Kilkari program, to identify bottlenecks to improve the efficiency of the program. In particular, we provide an initial analysis of the trajectories of beneficiaries' interaction with the mHealth program and examine elements of the program that can be potentially enhanced to boost its success. We cluster the cohort into different buckets based on listenership so as to analyze listenership patterns for each group that could help boost program success. We also demonstrate preliminary results on using historical data in a time-series prediction to identify beneficiary dropouts and enable NGOs in devising timely interventions to strengthen beneficiary retention.

Optimizing user engagement is a key goal for modern recommendation systems, but blindly pushing users towards increased consumption risks burn-out, churn, or even addictive habits. To promote digital well-being, most platforms now offer a service that periodically prompts users to take breaks. These, however, must be set up manually, and so may be suboptimal for both users and the system. In this paper, we study the role of breaks in recommendation, and propose a framework for learning optimal breaking policies that promote and sustain long-term engagement. Based on the notion that recommendation dynamics are susceptible to both positive and negative feedback, we cast recommendation as a Lotka-Volterra dynamical system, where breaking reduces to a problem of optimal control. We then give an efficient learning algorithm, provide theoretical guarantees, and empirically demonstrate the utility of our approach on semi-synthetic data.

As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. To represent the posting behaviors and social relations, the graph convolutional neural networks are applied to model influencers with heterogeneous networks during different historical periods. By learning the network structure with the embedded node features, InfluencerRank can derive informative representations for influencers at each period. An attentive recurrent neural network finally distinguishes highly effective influencers from other influencers by capturing the knowledge of the dynamics of influencer representations over time. Extensive experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months. The experimental results demonstrate that InfluencerRank outperforms existing baseline methods. An in-depth analysis further reveals that all of our proposed features and model components are beneficial to discover effective influencers.

Yes, we know you've heard it. And keep your brand ready for what's coming on Twitter. We've compiled a list of 101 global Twitter influencers who'll help you nail your engagements and build your brand presence on Twitter. Here's the badge for all the Twitter Influencers to show it off on social media Domain expertise & research interests have been around artificial intelligence, cybersecurity, the Internet of Things, blockchain, and sustainability. Area of interest includes Networks, Causal Inference, Machine Learning, AI, Big Data, Marketing, IT, Experiments, Social Commerce, Behavior Change, and Productivity.

During the first chatbot wave of 2016, the tool came with more frustrations than benefits. To this day, many people wince when they reach out to customer service and get an artificial response. Thing is, we're in the midst of a quieter but much more significant chatbot boom. The all-knowing chatbots we once thought to be the future have been replaced by specialized bots, and the results are outstanding. Of course, as chatbots' duties grow more sophisticated, so does their very definition.

The effectiveness of social media in facilitating quick and easy sharing of information has attracted brands and advertizers who wish to use the platform to market products via the influencers in the network. Influencers, owing to their massive popularity, provide a huge potential customer base generating higher returns of investment in a very short period. However, it is not straightforward to decide which influencers should be selected for an advertizing campaign that can generate maximum returns with minimum investment. In this work, we present an agent-based model (ABM) that can simulate the dynamics of influencer advertizing campaigns in a variety of scenarios and can help to discover the best influencer marketing strategy. Our system is a probabilistic graph-based model that incorporates real-world factors such as customers' interest in a product, customer behavior, the willingness to pay, a brand's investment cap, influencers' engagement with influence diffusion, and the nature of the product being advertized viz.

Influencer marketing has grown significantly due to the pervasive use of social media platforms in promoting products and services. In 2019 the practice reached $6.5 billion and is projected to reach $15 billion by 2022. Marketing today is all about algorithms, data and analytics to gain a targeted audience rather than the traditional spray-and-pray approach. The major success factor is figuring out how influencer marketing can become more effective by targeting the right audience to increase customer engagement. Technological advancements such as machine learning (ML), natural processing languages (NLPs) and artificial intelligence (AI) are changing how brands enhance influencer marketing. ML tech is assisting organizations in three areas: Creating relevant copy to reach the intended audience, identifying the right content creators for various marketing segments and recommending impactful workflow processes.

In the past, it was fairly easy to gain visibility and engagement on social media. But as the number of users and platforms increased quickly, competition stiffened. Moreover, social media has evolved from a place to connect with friends into a full-fledged publishing and advertising channel. It can get overwhelming to track countless metrics, like reach, engagement rates, and more. Since everybody is competing for limited audience attention, you need to keep a tab on your competitors' metrics as well.

From chatbots to discussion platforms, artificial intelligence (AI) is popping up at campuses all over the globe. In fact, the recent AI in Education Market Research Report from Research and Markets predicts that the global AI in education market will reach $25.7 billion in 2030, up from just $1.1 billion in 2019. The report shows that the largest demand for AI has been for learning platforms, mainly because of the increasing preference for remote and online education courses--even before the pandemic. It predicts that the next AI area to explode will be intelligent tutoring systems applications. A chatbot is a computer program that imitates human conversation and continually learns from every conversation it has, improving the efficiency of its responses.