Mining micro-influencers from social media posts

Micro-influencers have triggered the interest of commercial brands, public administrations, and other stakeholders because of their demonstrated capability of sensitizing people within their close reach. However, due to their lower visibility in social media platforms, they are challenging to be identified. This work proposes an approach to automatically detect micro-influencers and to highlight their personality traits and community values by computationally analyzing their writings. We introduce two learning methods to retrieve Five Factor Model and Basic Human Values scores. These scores are then used as feature vectors of a Support Vector Machines classifier. We define a set of rules to create a micro-influencer gold standard dataset of more than two million tweets and we compare our approach with three baseline classifiers. The experimental results favor recall meaning that the approach is inclusive in the identification

InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks

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.Comment: ICWSM 202

Importance of Social Network Structures in Influencer Marketing

As collaborations between brands and influencers become increasingly popular, predicting the capacity of an influencer to generate engagement has garnered increasing attention from researchers. Traditionally, managers have been relying on follower-based statistics to identify individuals with potential to reach a vast number of users on social-media. However, this approach may often direct managers to accounts with millions of followers accompanied with high recruiting costs. In this paper, we argue that the network structure of influencers is a useful measure for capturing an influencer’s ability to generate engagement. Using Instagram data, we perform a deep-learning analysis on the social network of influencers and show that the network structure explains a large share of the variations in user engagement, even outperforming traditionally used variables such as the number of followers in the case of comments. This study contributes to the emergent literature on the importance of social ties in the digital environmen

MIMIC: a Multi Input Micro-Influencers Classifier

Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performance of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.98 with our best performing model

Mekanisme pengurusan hutang dalam pembahagian harta pusaka orang-orang Islam di Malaysia

Pengurusan hutang merupakan tanggungjawab setiap individu untuk melangsaikannya. Namun masyarakat Islam di Malaysia kini memandang ringan mengenai tanggungjawab pengurusan hutang dalam menguruskan harta pusaka sehingga mengakibatkan timbulnya isu harta beku yang semakin meningkat peratusannya setiap tahun. Pengabaian menguruskan penyelesaian hutang dalam harta pusaka boleh berlaku disebabkan beberapa faktor antaranya, kedudukan dan status hutang si mati yang tidak jelas dan faktor daripada sikap tidak prihatin di kalangan ahli waris atau pentadbir yang dilantik menguruskan harta si mati. Salah satu faktor fenomena ini berlaku disebabkan ketidakfahaman pentadbir atau ahli waris terhadap prosedur di agensi pengurusan harta pusaka termasuk hal mekanisme untuk menyelesaikan hutang peninggalan si mati. Justeru itu, kajian ini bertujuan mengenal pasti prosedur pengurusan hutang si mati dalam pembahagian harta pusaka. Oleh itu, pengumpulan data dengan penggunaan kaedah kualitatif melalui kaedah temu bual dan analisis dokumen daripada fail kes digunakan di dalam kajian ini. Hasil kajian mendapati agensi pengurusan harta mempunyai bidang kuasa tertentu dalam menguruskan hutang peninggalan si mati bergantung kepada jenis dan kedudukan status harta dan hutang. Selain itu, didapati pengurusan hutang peninggalan si mati didapati lebih kompeten dikendalikan oleh Unit Pembahagian Pusaka Kecil (UPPK) manakala Amanah Raya Berhad pula lebih memainkan peranan sebagai Pentadbir harta pusaka si mati manakala Mahkamah Syariah pula lebih kompeten dalam urusan pengesahan ahli waris melalui perintah serta penentuan kadar bahagian ahli waris masing-masing termasuk Baitulmal. Beberapa cadangan turut dikemukakan di dalam kajian ini bagi meningkatkan pengetahuan umat Islam terhadap permasalahan hutang dalam harta pusaka dan meningkatkan perkhidmatan agensi-agensi yang berkenaan di Malaysia

Twitter interaction between audiences and influencers. Sentiment, polarity, and communicative behaviour analysis methodology

Twitter is one of several social networks with the highest numbers of users in Spain. In spite of this, how are communicative relationships developed in the digital environment among influencers who have emerged on the Internet? These personalities have a stronger influence on children and young people than traditional celebrities. The aim of this work is to study the communicative interaction generated on the profiles of Spanish influencers with the most followers on Twitter, based on the number of content items generated and the responses they receive from users. The polarity and sentiment conveyed by these communications have also been analysed. By processing publications in real time using machine learning based on sentiment analysis, 48,878 tweets and retweets from five influencers were studied over a period of 40 days. The results show that the publications reached nearly 200 million followers, and despite being fourth in terms of the number of followers, @IbaiLlanos is the influencer who leads the conversations on Twitter with the highest number of tweets, retweets, and audience share. Among the most popular topics, sporting events stand out. This study has also confirmed that the most frequently stated emotion is surprise, and that positive messages prevail over those that are negative and neutral with regard to polarity. Nevertheless, the linear regression data has verified that the main trend is toward publishing negative messages, with a lower statistical correlation, which is a behaviour that might possibly be duplicated on other social networks

Optimal Influencer Marketing Campaign Under Budget Constraints Using Frank-Wolfe

Influencer marketing has become a thriving industry with a global market value expected to reach 15 billion dollars by 2022. The advertising problem that such agencies face is the following: given a monetary budget find a set of appropriate influencers that can create and publish posts of various types (e.g. text, image, video) for the promotion of a target product. The campaign's objective is to maximize across one or multiple online social platforms some impact metric of interest, e.g. number of impressions, sales (ROI), or audience reach. In this work, we present an original continuous formulation of the budgeted influencer marketing problem as a convex program. We further propose an efficient iterative algorithm based on the Frank-Wolfe method, that converges to the global optimum and has low computational complexity. We also suggest a simpler near-optimal rule of thumb, which can perform well in many practical scenarios. We test our algorithm and the heuristic against several alternatives from the optimization literature as well as standard seed selection methods and validate the superior performance of Frank-Wolfe in execution time and memory, as well as its capability to scale well for problems with very large number (millions) of social users.Comment: accepted in IEEE Transactions on Network Science and Engineering, 16 pages, double column, 4 figure