Subscription-based online newspapers usually offer non-subscribed users a certain number of free articles in a period of time, and then directs them to a page (called paywall) asking for subscription. This approach (also known as metered or fixed paywall) does not consider the user's reading history nor the articles that the user may read in the future, and consequently, it may disengage many potential subscribers. To that end, we propose adaptive paywall mechanisms to make optimal paywall decisions (i.e., showing the article or the paywall) by balancing the benefit of showing the article against that of presenting the paywall. We define the notions of utility and cost which are used to define an objective function for the optimal paywall decision problem. We propose the Lookahead policy (LAP) and QPaywall policy (QP) as two data-driven approaches to solve the adaptive paywall problem. While the LAP method makes paywall decisions on the fly by simulating trajectories of article requests using Monte Carlo sampling, the QP approach is based on reinforcement learning and learns a neural network-based action-value (Q) function for this purpose. We compare advantages of the proposed approaches and discuss the practical considerations of using them in a real environment. Empirical studies on a real dataset from a major newspaper in Canada show that the proposed methods outperform several baseline approaches in terms of various business objectives.

Today, most newsreaders read the online version of news articles rather than traditional paper-based newspapers. Also, news media publishers rely heavily on the income generated from subscriptions and website visits made by newsreaders. Thus, online user engagement is a very important issue for online newspapers. Much effort has been spent on writing interesting headlines to catch the attention of online users. On the other hand, headlines should not be misleading (e.g., clickbaits); otherwise, readers would be disappointed when reading the content. In this paper, we propose four indicators to determine the quality of published news headlines based on their click count and dwell time, which are obtained by website log analysis. Then, we use soft target distribution of the calculated quality indicators to train our proposed deep learning model which can predict the quality of unpublished news headlines. The proposed model not only processes the latent features of both headline and body of the article to predict its headline quality but also considers the semantic relation between headline and body as well. To evaluate our model, we use a real dataset from a major Canadian newspaper. Results show our proposed model outperforms other state-of-the-art NLP models.

Many online news agencies utilize the paywall mechanism to increase reader subscriptions. This method offers a non-subscribed reader a fixed number of free articles in a period of time (e.g., a month), and then directs the user to the subscription page for further reading. We argue that there is no direct relationship between the number of paywalls presented to readers and the number of subscriptions, and that this artificial barrier, if not used well, may disengage potential subscribers and thus may not well serve its purpose of increasing revenue. Moreover, the current paywall mechanism neither considers the user browsing history nor the potential articles which the user may visit in the future. Thus, it treats all readers equally and does not consider the potential of a reader in becoming a subscriber. In this paper, we propose an adaptive paywall mechanism to balance the benefit of showing an article against that of displaying the paywall (i.e., terminating the session). We first define the notion of cost and utility that are used to define an objective function for optimal paywall decision making. Then, we model the problem as a stochastic sequential decision process. Finally, we propose an efficient policy function for paywall decision making. The experimental results on a real dataset from a major newspaper in Canada show that the proposed model outperforms the traditional paywall mechanism as well as the other baselines.