Predicting user reactions to Twitter feed content based on personality type and social cues

Journal Pre-proof Predicting user reactions to twitter feed content based on personality type and social cues Fabio R. Gallo, Gerardo I. Simari, Maria Vanina Martinez, Marcelo A. Falappa

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S0167-739X(19)30409-1 https://doi.org/10.1016/j.future.2019.10.044 FUTURE 5269

To appear in:

Future Generation Computer Systems

Received date : 11 February 2019 Revised date : 26 September 2019 Accepted date : 30 October 2019 Please cite this article as: F.R. Gallo, G.I. Simari, M.V. Martinez et al., Predicting user reactions to twitter feed content based on personality type and social cues, Future Generation Computer Systems (2019), doi: https://doi.org/10.1016/j.future.2019.10.044. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Elsevier B.V. All rights reserved.

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Predicting User Reactions to Twitter Feed Content based on Personality Type and Social Cues Fabio R. Galloa , Gerardo I. Simaria,b,∗, Maria Vanina Martinezc , Marcelo A. Falappaa a Department

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of Computer Science and Engineering, Universidad Nacional del Sur (UNS), and Institute for Computer Science and Engineering (UNS–CONICET) San Andres 800, Campus Palihue, (8000) Bahia Blanca, Argentina b School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University, Tempe, AZ 85281, USA c Department of Computer Science, Universidad de Buenos Aires (UBA), and Institute for Computer Science Research (UBA–CONICET), (C1428EGA) Ciudad Autonoma de Buenos Aires, Argentina

Abstract

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The events in the past few years clearly indicate that the modern social, political and economical landscapes are heavily influenced by how information flows through social networks. For instance, the recent outcomes of the US presidential elections and the Brexit vote show that misinformation and otherwise influencing content can affect events of great importance. In this paper, we adopt a simplified version of the recently proposed Network Knowledge Base (NKB) model to tackle the problem of predicting basic actions that a user can take given the content of their social media feeds: either take action (by reusing content seen in their feeds or creating new one), or otherwise take no action. We propose processing raw data obtained from social media based on the framework defined by the NKB model, and then formulate an action/no action prediction task that takes as input five features (including the user’s personality type and other social cues), and then go on to show—via an extensive empirical evaluation with real-world Twitter data—that machine learning classification algorithms can be successfully applied in this setting to make predictions about user reactions. The main result obtained is that, out of the features considered, personality type based on the Big-5 (also known as OCEAN) model is the most impactful; furthermore, though the rest of the features taken individually do not have a significant impact, the best results are obtained when they are all taken together. This is a first step in applying the NKB model towards understanding the effect of pathogenic social media phenomena such as fake news, how they spread via cascades, and how to counteract their ill effects.

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Keywords: Social Knowledge Bases, Social Networks, Hybrid Artificial Intelligence Models

Preprint submitted to Future Generation Computer Systems

September 26, 2019

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1. Introduction

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Communication among people, and between people and different kinds of organizations such as news outlets, political parties and candidates, and companies, has been revolutionized first by the Internet and then by the onset of the dominance of social media platforms. Understanding the way information flows through such platforms has become of key interest to anyone wishing to understand a given political, societal, or economical climate. Recent well-known examples of this are the Brexit vote in the United Kingdom and the US presidential elections: both occurred in 2016, had deep and lasting effects at a global scale, and their outcomes were largely driven by social media [1, 2]. In this paper, we adopt a model that was recently developed in the knowledge representation and reasoning community [3, 4]—the branch of Artificial Intelligence focused on logic-based reasoning—called Network Knowledge Bases (NKBs, for short) with the goal of understanding information flow in social media platforms. Though initial works have been focused on the theoretical problem of characterizing how users might change their beliefs as a response to the content they see in their feeds, in this paper we wish to adopt a basic version of the NKB model to address the problem of making high-level predictions about how users react to such content, with the same ultimate goal. We believe that gaining a deep understanding of what causes users to post new content, repost content from their feeds, or reuse it with a different purpose will take us closer to understanding how ill-intended presences such as misinformation campaigns, trolling and bullying, and voter suppression strategies can be fought. 1.1. The Network Knowledge Base Model

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Network Knowledge Bases [3, 4] are directed graphs G = (V, E), where vertices represent users and edges represent their relationships. Additionally, we have: • Labeling functions lvert for vertices and ledge for edges. Intuitively, both vertices and edges are labeled with attributes and their values; in the case of edges, labels also have assigned a number in the [0, 1] interval expressing the strength of the relationship.

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• A local knowledge base for each user, representing their current set of beliefs. The language used to represent such knowledge can vary in expressivity.

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• A set of integrity constraints characterizing undesired situations; such constraints are comprised of formulas that can make reference both to the

∗ Corresponding

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author Email addresses: [email protected] (Fabio R. Gallo), [email protected] (Gerardo I. Simari), [email protected] (Maria Vanina Martinez), [email protected] (Marcelo A. Falappa)

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The underlying network model can be seen as a multilayer (also known as multiplex) network, a model that has seen applications in many different fields [5]. Content posted by users in social media platforms are modeled by what we call news items, which are triples of the form (user, content, action), where the second component represents any kind of content, both multimedia such as pictures and videos, and text-based, such as status updates, links, comments. Other kinds of actions, such as “likes”, can also be modeled as news items. Finally, the third component is either add or remove. When users check their feeds on different social media sites, they are subject to a set of zero or more news items.

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network structure and the individual KBs. These can be simple data constraints such as vertices having more than one job (if this is a valid constraint). They can also be more complex, such as two spouses disagreeing on specific topics.

1.2. Working Hypothesis and Main Goal

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The main hypothesis of this paper is that the NKB model can be used in conjunction with personality type analysis and other social cues to build a hybrid system combining approaches from both knowledge representation and reasoning and machine learning capable of predicting high-level actions that users take as a reaction to the content in their social media feeds. As we will describe below, the NKB model used in our experimental evaluation is simple compared to the full power of the model since many of the rich features described in the previous section are not used; however, arriving at this albeit basic model requires a non-trivial amount of processing of the raw data in the tweets and follow network present in the dataset, and this processing is guided by the logic-based NKB framework. This is part of a larger effort to build a “map” of user types in social domains describing how they react to different kinds of content; the main goal is to understand how information flows through social media platforms so that the impact of malicious content (also commonly referred to as pathogenic social media) can be minimized. 1.3. Summary of Methods, Results, and Contributions

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Our main objective is to develop a principled way of making basic predictions of actions taken by users in reaction to their social media feeds, with the ultimate goal of understanding the influence of pathogenic social media phenomena such as fake news and how they spread via cascades, and how to counteract their ill effects. Figure 1 shows a general overview of the approach: we propose processing the raw data obtained from follow/friend relationships based on the general logicbased framework defined by the NKB model, and then formulate an action/no action prediction task that takes as input five features: (i) the user’s personality type according to the well-known Big-5 model (derived using the Personality Insights service provided by IBM Cloud), (ii) the time of day (segmented into

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intervals) in which the prediction is carried out, (iii) the predominant sentiment present in the feed (segmented into positive, negative, or neutral), (iv) the percentage of positive items present in the feed (segmented into four intervals), and (v) the percentage of negative items. We then go on to show via an extensive empirical evaluation with real-world Twitter data (covering several election periods in India) that machine learning classification algorithms can be successfully applied in this setting to make predictions about how users react to the content in their feeds. We do this by training a total of 49 classifiers based on tuning the hyperparameters of six different basic algorithms (Logistic Regression, Decision Trees, One Class Support Vector Machines, Random Forests, Multinombial Naive Bayes, and Complement Naive Bayes), and then carrying out a series of ablation tests to verify which of the features has the most impact in a series of experiments in which we vary also the size of the context (simulating how much the user remembers from their feed) and the number of users included in the training set (chosen by how active they are, to control class imbalance). The main results obtained are: (i) The best performance is obtained by a variant based on Complement Naive Bayes, with an F1 score of 0.4656, which is almost six times better than a baseline random guessing approach based on class proportions observed in the training data. Recall is generally very close to 1, while the best values of precision are close to 0.4, indicating that room for improvement lies in addressing false positives. (ii) Out of the features considered, personality type is the single most impactful, showing significant drops in recall (though, interestingly, precision is not significantly affected). Furthermore, though the rest of the features taken individually do not have a significant impact, the best results are obtained when they are all

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Figure 1: Overview of the general method proposed—Raw data produced in social platforms is processed to build a Network Knowledge Base, which along with a set of external services comprises a model of the user that is then applied in making a prediction about the user’s future actions based on their current context. In this initial work, the NKB component is simplified as described in the text.

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taken together. To the best of our knowledge, this problem has not yet been tackled in this way in the literature; our main contribution is therefore showing that logicand machine learning-based tools can be used together to obtain principled solutions to important problems in social media analysis. As we will discuss in the following section, there is a lot of work in the literature that addresses related problems, but this is generally done in an ad hoc manner—our approach is designed to work as a more general framework that can also address more than one problem at once. Future work beyond these first steps is of course needed. The rest of this paper is organized as follows: Section 2 discusses related work from several different communities that have tackled similar problems with different objectives; Section 3 discusses our approach in detail, and Section 4 presents the experimental setup that we designed to evaluate our hypotheses (Section 4.1) and the analysis of a selection of results (Section 4.2)—additional results can be found in Appendix A. Finally, Section 5 includes a closing discussion and future work.

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2. Related Work

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The vast amount of information that flows through social media platforms has been used for making different kinds of predictions in many fields, such as health, education, economics, politics, etc. In this section we first discuss related work in the literature on making predictions based on different kinds of cues present in social media, and then focus on how personality type and sentiment have also been leveraged in the past as part of forecasting efforts. 2.1. Predictions based on Social Media

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Health and well-being. It has been shown that certain psychological disorders can be detected by analyzing cues present in social media profiles; [6] studies the systematic examination of addiction-like symptoms related to online social media based on the evaluation of behavioral patterns of Facebook users. Similarly, [7] examines the relation between online social network belongingness and obsessive-compulsive disorders. Economics and user behavior. From a different point of view, social media can be seen as a way to take snapshots of a society’s mood—thus, the analysis of this kind of data has been used as a way to forecast economic and political phenomena. For instance, [8] investigates whether collective mood states that are derived from Twitter feeds can be correlated to the value of the Dow Jones Industrial Average over time. Focusing on the relationship between people and the different tools offered in social media platforms, [9] carried out an experiment to identify the relationship between adoption of Twitter and prior engagement in

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Different kinds of events based on signals (or features) present in social media streams have been considered in a wide variety of communities:

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Cascades. A cascade is a viral effect that leads to content being spread beyond the boundaries of the typical; this kind of decentralized process is one of the main strategies for information diffusion in social media because it causes chain reactions involving many users. Therefore, this phenomenon has caught the interest of many researchers looking to interpret and understand the factors behind it. Such cascade effects have been studied and characterized in many different online social networks, such as Facebook [17, 18], Twitter [19], and LinkedIn [20]. An interesting approach is taken in [21], where a recommender system is built that predicts the likelihood that a user will retweet information when asked to (for instance, for spreading alerts) and recommends the top-n users to engage with. The work of [22] is perhaps the closest to ours, though their main focus is at a different level—two frameworks are proposed: the first is for predicting whether a certain user will retweet a message relayed by friends, and the latter is for predicting the popularity of tweets. The main characteristic of this approach is that it does not necessarily require global network information; a new set of community-related features are identified that afford considerable improvement in retweet prediction accuracy. Though in this work we also deal with the problem of predicting accuracy of retweets and generation of own content,

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Politics. When an electoral process is going to be held, the public typically expresses their opinions on social media. This context can be seen as a massive “exit poll-like” source of information for predicting possible results automatically. For instance, [13] uses Twitter mentions of political parties and demographic information as an additional feature; another example is [14], which leverages information about social circles. The phenomenon of “hot topics” (those that are broadly discussed by a large number of users) in social media is also of interest—predicting these topics’ general direction and lifetime would therefore be of interest in this domain. A generic framework is presented in [15] to identify both the type of a given campaign and the different activities that were carried out during its lifetime, such as meetings, calls for action, etc., focusing particularly on climate change campaigns on Twitter. An interesting aspect of that work is the development of a model to predict the presence of a campaign in an individual tweet. Finally, a related approach is taken in [16], where the authors analyze Twitter discussions of a presidential election using approaches based on network science, sentiment analysis, and bot detection.

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other types of online activities. In a similar vein, the recent work of [10] analyzed online consumers’ purchase intentions and investigated their structural positions by analyzing their friendships in social networks, integrating the social network and the planned behavior theories. On the commercial side, [11] forecasts boxoffice revenues for movies by considering the rate at which tweets are created about particular topics—the results showed improvement when the tweets were analyzed for sentiment. In a broader application field, [12] defines a model that includes a set of unique features derived from Twitter (namely network, activity, user, and tweet content) and, based on these features, a supervised machine learning solution for detecting cyberbullying is developed.

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our eventual goal is to identify user types; our hypothesis is that by building a map describing how users tend to behave in different situations, we can better understand how pathogenic social media campaigns can be mitigated. This line of work started in [23] and later continued in [3] and [4] as part of the development of belief revision operators; in particular, [4] already includes preliminary experiments on detecting user types based on a much simpler approach than the one developed in this paper. Finally, for a recent survey of work on predictions using social media, with a special focus on identifying pitfalls and best practices, see [24].

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2.2. Big-Five and Sentiment Analysis as bases for Making Predictions

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Personality detection in social media has been a central research topic because it has proven to be a useful tool in prediction tasks. The analysis of personality traits based on social network information has been studied for some time now: [25] presents a method to predict users’ Big Five personality traits (cf. Section 3 for a description of the Big-Five/OCEAN model) through publicly available information on their profiles. Similarly, [26] proposes multi-task regression and incremental regression algorithms to predict Big-Five personality scores from online user behaviors; these algorithms are tested in an experiment over a Sina Weibo1 dataset. See [27] for a recent survey of techniques for identifying personality traits using social media. The work of [28] presents a causal framework focusing on distinguishing influence in social media from other confounding variables such as similarity (two people might behave similarly because they share many traits), features of the item per se (for instance, certain messages are engineered to be enticing to many people), or other external circumstances. One important aspect in the study of influence is the effect that brands have on users; for applications of sentiment analysis in the context of brands, see [29, 30]. In the context of the Big-Five model, which is centered on evaluating an individual’s openness, conscientiousness, extraversion, agreeableness, and neuroticism, some personality traits have been identified as being more relevant for predicting certain behaviors. For instance, according to [31], people with high openness and low neuroticism responded more favorably to a targeted advertisement. As reported by [32], extraversion and openness are positively related to social media use, while emotional stability was a negative predictor; similar results were obtained in other studies—see [33]. These findings can be considered to be in line with [34], where a core subset of the Big-Five traits is identified that is sufficient to obtain adequate approximations of a person’s scores. Sentiment and personality analysis has also been used for predicting consumer behaviors [35]. Since sometimes the information available on users is not enough for getting acceptable prediction accuracy with traditional techniques, [36] develops a model that integrates word embedding features with Gaussian process regression to improve prediction of personality trait values with up to 8

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3. NKBs for Prediction of User Reactions to their Social Media Feeds

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As we discussed in Section 1, Network knowledge bases are comprised of a multilayer network, each with a local knowledge base, and a feed of news items. In this section, we describe how this model can be used to answer the question: given past behavior and the current context, what can we expect a given user to do in the following time step? Towards this end, we selected a set of four features that can be easily analyzed. (1) Personality Type. The OCEAN model of personality traits (also known as Big-Five) [43] is based on assigning a score to each of the following dimensions of a person’s character and how they engage with the world; we provide a brief description based on [33]:

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times fewer data than previous techniques. Similar to the discussion in the previous section, another domain in which personality has been used for predictions is in health-related issues. For instance, [37] analyzes changes in personality outcomes as features that can be leveraged to detect depressive disorders. Furthermore, according to results reported in [38], Big-Five personality traits have a meaningful relation with Internet-related pathologies [39], as well as in the characterization of Facebook users [40]. Finally, a computational model for predicting actions and sentiment toward controversial topics is defined in [41]. Predictions are carried out considering sentiment, opinion, and action, and the relation among them. Particularly, [42] analyzes Big-Five personality traits for identifying users’ political party affiliations. For a survey on detection of personality traits in social media, see [31].

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• Openness: Extent to which the person is open to experiencing new activities. Other facets associated with a high score of openness are adventurousness, artistic interests, depth of emotions, imagination, intellectual curiosity, and tolerance for diversity. • Conscientiousness: Tendency to act in an organized, thoughtful manner. Other facets associated with a high score in this dimension are driven, deliberateness, dutifulness, orderliness, self-discipline, and self-assured.

• Extraversion: A person’s tendency to seek stimulation while in the company of other people. Other aspects associated with a high value of extraversion are: energetic, assertiveness, cheerfulness, friendliness, and gregariousness.

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• Agreeableness: Defined as a tendency to be compassionate and cooperative toward others; high values of agreeableness are also associated with: altruism, cooperativeness, modesty, empathy, and trusting.

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• Neuroticism: Also referred to as emotional range or natural reactions, neuroticism refers to the extent to which a person’s emotions are sensitive 8

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to their environment. High values of neuroticism are associated with: anger, anxiety, melancholy, moodiness, self-consciousness, and sensitivity to stress.

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As described in the following section, we based our model on IBM Cloud’s Personality Insights service, which provides a real value in the unit interval for each dimension; a score above 0.5 reflects a greater than average tendency; therefore, in order to discretize the space of possible personality types, we divide the possible scores into high and low using this threshold, leading to 25 = 32 possible personality types in our approach.

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(2) Time Step/Interval. It is also necessary to discretize time into steps or intervals; this can be as granular or coarse as desired, from seconds or less to hours, days, or weeks. In the following, we refer to a collection of such intervals selected from the most recent up to a certain point in the past as the context taken into account for the prediction task.

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Figure 2 illustrates these features in the context of a prediction task, which focuses on identifying whether a user will either take action or not—here, by “action” we refer to generation of content of one of the following types: (i) using a hashtag that does not appear in the selected context (we call this new content; (ii) reusing a hashtag appearing in the context with the same sentiment as the predominant one associated with it; or (iii) reusing a hashtag from the context with a change in sentiment. Figure 3 illustrates this definition of the prediction task; parameter k determines the number of intervals that will be contained in the context. Building a Basic NKB. Given this setup, we can build a simple NKB instance by defining the underlying graph based on the follower relation, and a single label Twitter follower with weight 1. News items are then built based on the length of the time step (parameter k, as shown in Figure 3) and each user receives the news items generated by those whom they follow. In this paper we assume that features 1–5 illustrated in Figure 2 make up a state of the user at each time point, and that this state represents a high-level state of the local knowledge bases in the NKB model (in lieu of having its explicit content). Note that this is a simple NKB model since it only uses a single relation with a constant weight, and an abstraction of the content of the local knowledge bases.

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(4) Sentiment Distribution. As a refinement of the previous feature, we consider the distribution of positive/negative sentiment to be of importance as a measure of how strong the predominant sentiment is in the context in question. Once again, this is discretized, in this case into intervals [0, 25), [25, 50), [50, 75), [75, 100] indicating the percentage of news items with positive sentiment, and another analogous value for negative sentiment.

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(3) Predominant Sentiment. We can analyze the overall sentiment or tone present in each news item and classify it, for instance, into positive, negative, and neutral. Then, given the context, it is natural to consider the overall tone that the user was exposed to—we call this the context’s predominant sentiment.

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Figure 2: Overview of the features considered in our model; (1)–(5) are used as the inputs to machine learning classifiers tasked with predicting the value of (6).

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Nevertheless, we will show that this model, which is built by carrying out a nontrivial amount of processing of the raw data guided by a logic-based approach, is already capable of solving an interesting prediction problem—incorporating additional features to evaluate the full power of the NKB model, such as data from more than one social platform, relationships with different degrees of strength, and explicit content of the local knowledge bases, is the topic of ongoing and future work.

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4. Experimental Evaluation

4.1. Experimental Design

4.1.1. Dataset The Twitter dataset that we used for our experimental evaluation contains 18,292,721 posts published between July 15, 2013 and March 25, 2015; for this experiment, we selected only the posts that were in English, which were the vast majority (16,780,489). As described in Section 3, in order to track user reactions, we focus on tweets containing hashtags—in our dataset, hashtags are

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We now report on the empirical evaluation of our approach; in Section 4.1 we discuss the design of the experiments, and Section 4.2 contains a selection of the most relevant results; for reasons of space and readability, we include the full set of results in Appendix A. The experiments were run on a computer with an AMD A8-7650K Radeon R7 processor at 3.3GHz with 10 compute cores (4 CPU + 6 GPU) and 4GB of RAM, using Python 3.6.4 with the sklearn v0.2 library.

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White shapes represent the latest news items that are not part of the context for this iteration

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Figure 3: Prediction task (classifier input/output): The content of a user’s Twitter feed is represented as a stream of shapes flowing from left to right; time is divided into 12-hour intervals, and the value of parameter k determines the context for the prediction task. During the prediction interval, the user either takes action or no action—the former comprises either generating new (given the context) content, or reusing content from their feed.

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4.1.2. Data Preparation For each tweet containing a hashtag, we create a news item for that hashtag, origin user who posted, and either add or remove according to the sentiment expressed in the text (see below). Then, in order to build the setup illustrated in Figure 3, we prepared each user’s news item feed comprised of the tweets posted by users they follow—note that, in the period covered by the dataset, there may exist cases in which the follow relationship was not yet established at the time of the posting; this is a limitation inherent to the data collection effort. The news item feed thus derived for each user is the basis for preparing the context used in the prediction task (cf. Figure 3); as it will become clear in the

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present in 5,107,986 tweets, with a grand total of 136,809 distinct hashtags. The dataset also contains information on the underlying network of follow/friend relations. Though this dataset was originally collected as part of an effort to analyze elections in India2 , our experiments seek to understand how users react to incoming information; the content and domain of posts is not of specific interest, but we point out that the same kind of analysis should be performed on other datasets to see if there are confounding variables affecting our results— this is part of ongoing work.

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thank V.S. Subrahmanian for sharing the dataset with us.

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Hyperparameters n/a n/a kernel=linear kernel=rbf, gamma=0.1 kernel=rbf, gamma=0.2 kernel=rbf, gamma=0.3 kernel=rbf, gamma=0.4 kernel=rbf, gamma=0.5 kernel=rbf, gamma=0.6 kernel=rbf, gamma=0.7 kernel=rbf, gamma=0.8 kernel=rbf, gamma=0.9 kernel=rbf, gamma=1 min samp leaf=1, n est=10 min samp leaf=1, n est=50 min samp leaf=1, n est=100 min samp leaf=5, n est=10 min samp leaf=5, n est=50 min samp leaf=5, n est=100 min samp leaf=10, n est=10 min samp leaf=10, n est=50 min samp leaf=10, n est=100 min samp leaf=20, n est=10 min samp leaf=20, n est=50 min samp leaf=20, n est=100

ID Algorithm Hyperparameters C26 Multinomial NB alpha=0, fit prior=True C27 ” alpha=0, fit prior=False C28 ” alpha=0.05, fit prior=True C29 ” alpha=0.05, fit prior=False C30 ” alpha=0.1, fit prior=True C31 ” alpha=0.1, fit prior=False C32 ” alpha=0.15, fit prior=True C33 ” alpha=0.15, fit prior=False C34 ” alpha=0.2, fit prior=True C35 ” alpha=0.2, fit prior=False C36 ” alpha=100, fit prior=True C37 ” alpha=100, fit prior=False C38 Complement NB alpha=0, norm=True C39 ” alpha=0, norm=False C40 ” alpha=0.05, norm=True C41 ” alpha=0.05, norm=False C42 ” alpha=0.1, norm=True C43 ” alpha=0.1, norm=False C44 ” alpha=0.15, norm=True C45 ” alpha=0.15, norm=False C46 ” alpha=0.2, norm=True C47 ” alpha=0.2, norm=False C48 ” alpha=10, norm=True C49 ” alpha=10, norm=False

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Algorithm Log. Regression Decision Tree One Class SVM ” ” ” ” ” ” ” ” ” ” Random Forest ” ” ” ” ” ” ” ” ” ” ”

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ID C01 C02 C03 C04 C05 C06 C07 C08 C09 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25

Figure 4: Full list of classifier configurations considered in the evaluation.

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4.1.3. External Services In order to obtain a value for the personality type feature described in Section 3, we used the Personality Insights service provided by IBM Cloud3 4 . This API analyzes text in order to derive a personality profile of its author; though the service provides profiles for three models (Needs, Values, and Big5/OCEAN), in this paper we only focus on the Big-5 model, as discussed in Section 3. For each user in our dataset, we built one text file by concatenating all their posts, and submitted it to the API; then, as discussed above, we discretized the values for each dimension into high and low (or “+” and “–”) to obtain a value between 1 and 32. To detect the general tone of a post, we used the PHPInsight tool5 , which yields a value of either positive, negative, or neutral.

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discussion of the parameters below, this mapping from actual Twitter feeds to contexts can result in certain discontinuities since, for instance, it might be the case that a user did not receive any news items in the relevant context nor did they take any action.

3 https://www.ibm.com/watson/services/personality-insights/

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4 We thank Constanza Caorsi from IBM Argentina for facilitating an academic license for this service. 5 https://github.com/JWHennessey/phpInsight

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4.1.4. Classifiers and Hyperparameters In order to explore the space of possible algorithms and their specific settings, we trained classifiers using the following basic algorithms and hyperparameter variations; as usual, we use 90% of the available data for training, and the remaining 10% for testing. • Logistic Regression (no hyperparameters). • Decision Tree (no hyperparameters).

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– kernel: linear or radial basis function (rbf).

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• Random Forest, with hyperparameters:

– Minimum number of data points allowed in a leaf node (min samples leaf) varied in {1, 5, 10, 20}. – Number of trees in the forest (n est) varied in {10, 50, 100}.

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• Complement Naive Bayes, with hyperparameters: – alpha (smoothing parameter) varied in {0, 0.05, 0.1, 0.15, 0.2, 10}.

– norm (second normalization of weights performed), which can be either true or false. Figure 4 shows the full set of configurations arising from these combinations of basic algorithms and hyperparameter tuning. Section 4.2 reports on the performance of the best among these 49 configurations.

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4.1.5. Other Experimental Parameters Finally, the following three parameters are also part of our experimental setup: 400

Number of intervals in the context (k): This parameter was varied from 1 to 4; we often present its values in descending order since we consider it natural to think of contexts in descending order of amount of information available.

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4.1.6. Prediction Task (Classifier Input/Output) With all this information in place, the prediction task that we are interested in carrying out is: given values for the features shown in Figure 2 (representing a user’s current context), can we predict whether the user will take action (which might take the form of directly reusing content from their feed, reusing it with a change in sentiment, or producing new content), or otherwise take no action? The overall approach to solving this task by invoking machine learning classifiers once the data is prepared as described above is illustrated in Figures 2 and 3.

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little (nothing, or next to nothing), as well as many who are very active in their posting habits. Therefore, if data for all users is considered for the prediction task, this can lead to a severe class imbalance; Figure 5 (right) shows the average percentage of intervals for which action/no action was taken for each value of k considered (recall that intervals are discarded for which nothing is received in a user’s feed and no action is taken during the prediction interval, so the value of k influences the number of users chosen). In order to investigate the effect that this class imbalance has on the performance of our classifiers, we selected users according to a parameter that we call spread; a user will be chosen according to a value of x for this parameter if the difference (in percentage points) between the percentage of intervals for which action was taken vs. no action was taken is at most x. So, for instance, for a value of 60, a user with 23% intervals with action and 77% with no action is selected (since 77 − 23 = 54 ≤ 60), but one with 19%–81% is not (since 81 − 19 = 62 > 60). Figure 5 (left) shows the number of users selected for a variation of the parameter from 50 to 100 in steps of 5, for each of the values of k—this variable denoting number of users participating in the prediction task (both in training and testing) will be referred to as #Users. As we will see in Section 4.2, this parameter has a visible impact on the performance of the classifiers in the prediction task.

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4.1.8. Evaluation of Feature Impact In order to evaluate the impact that the presence of each feature has on the performance of the classifier, for most parameter settings we trained the following variations:

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4.1.7. Evaluation Metrics In order to evaluate the performance of each classifier, we use the typical metrics of precision (ratio of true positives to overall positives, indicating the proportion of the selected elements that are relevant), recall (ratio of true positives to the sum of true positives and false negatives, indicating the proportion of relevant elements that are selected), F1, and Fβ . The latter two are summary metrics that combine precision and recall into a single value. F1 is the harmonic mean of the two values and weighs them equally; Fβ , on the other hand, assigns weights according to the value of β—the higher the value, the more importance it places on recall (and vice versa). In our experiments, we vary β ∈ {0.5, 2, 3, 4}.

• ALL: All features discussed above are included.

• WOO (without OCEAN): All features except OCEAN. • WOI (without Interval): All features except interval.

• WOS (without sentiment features): All features except predominant sentiment and sentiment distribution.

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• JO (just OCEAN): Only the OCEAN personality type is considered. In the following section, we compare the performance of the ALL variant against the others. 4.2. Experimental Results

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Note: All scatterplots reported in this section and the appendix are of the form “ALL vs. X”, where X ∈ {WOO, WOI, WOS, JO} (as described in Section 4.1.8), and plot precision, recall, F1, or Fβ for a fixed value of k (number of intervals in the context). Each chart contains three plots: a solid line for ALL, a dashed line for X, and a dotted line showing the percentage change from one to the other. The x-axis for all charts is #Users, and is plotted on a logarithmic scale to show greater detail at the lower end, where the most interesting results can be found. Finally, all plots in this section include the results of statistical significance tests (two-tailed two-sample equal variance t-tests, with p-values rounded to four decimals) to show the confidence with which we can state that “ALL vs. X” yield different performance. These results are indicated in the title of each

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We now discuss the most important results from our empirical evaluation; the full set of results is available in Appendix A. The first set of results is concerned with determining which of the 49 classifier configurations performed best overall.

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Selected classifiers (All features) ̶ Average F1 values for selected range of #Users

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4.2.1. Overall Classifier Configuration Performance Figure 6 shows a plot of 8 selected classifiers of the ALL variety; the criterion for selection involved including at least one classifier for each basic algorithm, and then selecting the overall best performers according to the F1 metric for the number of users selected when spread is between 50 and 70 (these values were chosen because they generally yielded the best performance over all classifier configurations). Though there is a clear winner—C42’s performance beats all others6 for all values of k—another interesting result to note is that different classifiers responded differently to the variation of the number of intervals in the context. Though intuition dictates that more information leads to better performance (as in the case of C04), there are classifiers for which no clear relationship is observable (C05 and C36) or—the majority—for which the opposite holds (C01, C02, C20, C25, and C42). This supports the hypothesis that users tend to react

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graph, and can be either highly significant (in which the p-value obtained is less than 0.01, denoted with “∗∗”), significant (in which the p value obtained is less than 0.05 but greater than 0.01, denoted with “∗”), or not significant (in which the p value obtained is greater than 0.05, denoted with “NS”).

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6 Pairwise comparisons between C42’s and every other classifier’s average over all k value using two-tailed two-sample equal variance t-tests yielded statistically significant differences with p-values below 0.01.

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Comparison with random guessing as a baseline. As shown in Figure 5 (right), there is roughly a 8%/92% occurrence of action/no action in the dataset, so a classifier simply trying to randomly guess based on this distribution would have precision and recall (and thus F1) of about 0.08. Therefore, C42’s F1 value of 0.4656 (average over all variations of k) is about 5.8 times better than random guessing. The best performance achieved, which corresponds to C42 for k = 1, achieves an F1 value of 0.4905, boasting a 6.1x increase over random guessing. Even the worst performer in this regard, C04 with average F1 over all values of k of 0.3736, is still about 4.6 times better than this baseline. This shows that the general approach of considering personality type and other social cues in combination with our network knowledge base model is a solid approach to predicting basic user reactions to their social media feeds.

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performance decreases progressively (cf. the second column of charts, which plot the F1 metric for all cases). Another clear conclusion is that the feature that has the most impact on performance is personality type. Though the second and third rows seem to suggest that not including information on interval or segment would actually benefit performance, the comparison in the last row against JO clearly indicates that the two features in conjunction are very valuable. Finally, a very interesting result to note is that removing the most impactful feature (personality type) has virtually no impact on precision; this can be observed in the plots in Figure 8, which show the results for the variations of number of users and number of intervals in the context. Combined with the above observations, this means that adding the OCEAN personality type feature affords a sizeable boost in recall without affecting precision.

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4.2.4. Best configuration: Varying k and OCEAN Feature Impact on Fβ Finally, in order to illustrate the impact of the false positive and false negative rates on performance, we plot Fβ for the ALL vs. WOO comparison in Figure 11. Intuitively, the Fβ metric generalizes F1 to make it possible to focus more either on precision or recall; we can see that for β = 0.5 the plots show less difference between ALL and WOO because it is focusing more on false positives than false negatives. On the other hand, as seen in the previous sections, the addition of the personality type feature has most impact on lowering the false negative rate, so for β = 2, 3, 4 the plots show an increasing separation between the curves. 5. Conclusions and Future Work

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In this paper, we have proposed the use of the Network Knowledge Base (NKB) framework—which was initially proposed as part of an effort to model how users or agents change their beliefs in the presence of new information in their social domains—as the basis for an approach to predict how users react to content in their social media feeds. Our proposal involves the arrangement of available data into a simple instance of an NKB and a stream of news items for each user (representing the content of their social media feeds) and then identifying several social cues as features to train a classifier to predict whether or not a user will take action in a given time period. Our results show that such an approach combining an albeit simple logic-based artificial intelligence model and machine learning tools can lead to effective reasoning tools. This is part of a larger effort to understand how information flows in social platforms so that pathogenic social media (such as fake news, trolling, and voter suppression campaigns, among many others) can be mitigated. Future work therefore involves further expanding the experimental evaluation started in this paper; in particular, we would like to expand the number of datasets so that we can minimize the effect of potential confounding variables such as the main topics being discussed, nationality, language, and others. We would also like to incorporate more features in order to further improve performance; for instance, by adding expressive power afforded by the NKB model such as weighted relations and representing the explicit content of the individual knowledge bases, the context can be greatly enriched by taking into account the nuances of each relation, as well as the current beliefs of each user. Finally, we are currently working on developing a theoretical characterization of how users update their beliefs in the presence of new information—see [4, Figure 3] for a roadmap of this overall research program.

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Fabio R. Gallo is a doctoral student at Universidad Nacional del Sur. His research interests are centered in knowledge representation and reasoning, with a special focus on social knowledge. Contact him at: [email protected].

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Gerardo I. Simari is an assistant professor in the Department of Computer Science and Engineering, Universidad Nacional del Sur, a researcher in the Institute for Computer Science and Engineering (Universidad Nacional del SurCONICET), and adjunct faculty at the School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University. His research interests include reasoning under uncertainty, preferences, social knowledge, and databases. He holds a Ph.D. from University of Maryland College Park. Contact him at: [email protected].

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Maria Vanina Martinez is an assistant professor in the Department of Computer Science, Universidad de Buenos Aires, and a researcher at the Institute for Computer Science (Universidad de Buenos AiresCONICET). Her research interests are at the intersection of knowledge representation and reasoning and database theory, with a special focus on reasoning with inconsistent and uncertain knowledge. She holds a Ph.D. from University of Maryland College Park. Contact her at: [email protected].

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Marcelo A. Falappa is an associate professor and dean of the Department of Computer Science and Engineering, Universidad Nacional del Sur, and a researcher at the Institute for Computer Science and Engineering (Universidad Nacional del SurCONICET). His research interests are centered in belief revision and argumentation-based reasoning. He holds a doctorate from Universidad Nacional del Sur. Contact him at: [email protected].

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Journal Pre-proof - Use multi-layer network model to represent social network data and information flow - Model prediction of user reactions to social media content as ML classification - Leverage several social cues and OCEAN personality type as main features

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- Extensive empirical evaluation on Twitter data shows effectiveness of approach

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Declaration of interests    ☒ The authors declare that they have no known competing financial interests or personal relationships  that could have appeared to influence the work reported in this paper.    ☐The authors declare the following financial interests/personal relationships which may be considered  as potential competing interests:    

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