The rise of fake news in the past decade has brought with it a host of consequences, from swaying opinions on elections to generating uncertainty during a pandemic. A majority of methods developed to combat disinformation either focus on fake news content or malicious actors who generate it. However, the virality of fake news is largely dependent upon the users who propagate it. A deeper understanding of these users can contribute to the development of a framework for identifying users who are likely to spread fake news. In this work, we study the characteristics and motivational factors of fake news spreaders on social media with input from psychological theories and behavioral studies. We then perform a series of experiments to determine if fake news spreaders can be found to exhibit different characteristics than other users. Further, we investigate our findings by testing whether the characteristics we observe amongst fake news spreaders in our experiments can be applied to the detection of fake news spreaders in a real social media environment.

• Computing methodologies → Machine learning; • Applied computing → Psychology.

Fake News; Fake News Spreader; Psychological Theories; Social and Behavioral Studies; Social Media

ACM Reference Format: Mansooreh Karami, Tahora H. Nazer, and Huan Liu. 2021. Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors. In Proceedings of the 32nd ACM Conference on Hypertext and Social Media (HT ’21), August 30–September 2, 2021, Virtual Event, Ireland. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3465336.3475097

Due to the increasing amount of our time spent on social media platforms, it is no surprise that people tend to receive their news content through social media more than before. One in five U.S. adults used social media as their main source of political and election news for the US presidential election in 2020 [29]. The high rate of engagement with online news can be mainly attributed to the nature of the social media platforms themselves. Social Media is typically inexpensive, provides easy access to users, and supports fast dissemination of information that is not possible through traditional media outlets. However, despite these advantages, the quality of news on social media is considered lower than that of traditional news outlets. A factor contributing to this low quality is the widespread nature of fake news articles online. Fake news is a piece of false information published by news outlets to mislead consumers [40, 55].

Fake news has several significant negative effects on civil society. First, people may accept deliberate lies as truths. The likelihood of accepting fake news as true increases after repeated exposure [16], especially when the content aligns with the user’s beliefs [17, 53]. Second, fake news may change the way people respond to legitimate news. When people are inundated with fake news, the line between fake news and real news becomes more uncertain. Fake news spreaders make users doubt the nature of real news and create the idea that everything is biased and conflicted, and it is impossible to distinguish fake from real news [25]. Finally, the prevalence of fake news has the potential to break the trustworthiness of the entire news ecosystem. For instance, despite traditional domains such as the New York Times, the Washington Post, and CNN being among the most shared COVID-19-related stories on Twitter, a fake news domain, Gateway Pundit, was ranked 4th in August and 6th in September of 2020 among the most shared domains for URLs about COVID-19 [23]. Therefore, it is critical to develop methods that detect and mitigate fake news, with the purpose of benefiting the general public and the entire news ecosystem.

Detecting fake news is a challenging task because it is designed to be indistinguishable from real news and intentionally misleading. As a result, the features extracted from the content are not enough to build an accurate detection method. Researchers have tackled this problem by using the characteristics of posts, user networks, and user profiles. However, factors that motivate users to spread fake news on social media and their importance in fake news detection have remained understudied. Studies [11, 35] have shown that users are more prone to propagating fake news stories when the situation is uncertain, they are emotionally overwhelmed and anxious, the topic of discussion is of personal importance to them, and they do not have primary control over the situation through their actions. In this work, we provide a methodology to extract factors that are not reliant on currently existing self-reported survey methods [5, 45]. Our analysis aims to shed light on the attitude of spreading fake news, rather than detecting potential fake news among the users’ tweets. We investigate the following research questions:

RQ1: What motivates users to spread fake news?

RQ2: What are the differences between users who spread fake news and the users who spread real news in terms of motivational factors determined in RQ1?

RQ3: Can we use the differences between fake news spreaders and other users to profile fake news spreaders?

We make the following contributions in this work:

We study a novel problem by investigating the relationship between motivational factors and spreading fake news on social media in Sections 2 and 3. This study lays the foundation of exploiting the effectiveness of these factors for preventing the dissemination of fake news;

We perform a statistical comparative analysis between users who spread fake news and other users. We hypothesize that users who are keen to spread disinformation may have different motivating characteristics compared to other users. Our study shows that they are significantly different in terms of motivational factors in Section 4.1;

We conduct experiments on real-world datasets to show whether the extracted features add additional value to the representation of the users’ tweets in Section 4.2.

To investigate the factors that motivate users to spread fake news on social media (RQ1), we begin with enumerating a list of motivational factors in spreading fake news: uncertainty, anxiety, lack of control, relationship enhancement, and rank.

2.1 Uncertainty

2.2 Emotions

2.3 Lack of control

2.4 Relationship Enhancement

2.5 Rank

We use two datasets from FakeNewsNet repository [41] to explain our method and experiments in Section 4: PolitiFact and GossipCop[^1]. The data consists of fake and real stories identified by PolitiFact and GossipCop websites. To identify the users, we utilized a crawler to collect all the tweets referring to those stories. Recent posts, profile, and list of followers and friends of Twitter users who posted about fake and real stories were also collected. We treat each user as a document and the users’ recent posts are considered as the content of that document. We based this method on the experiments done by Yang et al. [54], using the same parameter of about 150 words per document. Since each tweet consists of at most 280 characters (which on average is about 55 words), if there is a mention or retweet of three or more fake news, that user is tagged as a user who is keen to spread fake news, otherwise, that user is labeled as real news spreader. All the features introduced in Section 2 can be extracted from the content (tweets), activity (tweeting behavior and likes), or network (followees and followers) of Twitter users in PolitiFact and GossipCop datasets.

[^1]: The source code for this work is available at https://github.com/mansourehk/profiling.

Table 1: Statistics of the datasets.

3.1 Feature Extraction

We pre-processed all tweets to remove punctuation, URLs, hashtags, and mentions. To measure a feature using an LIWC category, we find the percentage of words in a tweet that belongs to that category. Next, we explain how each of the features are measured:

Selected LIWC features: there are three LIWC categories that are related to uncertainty: Discrepancy (e.g. should, would, and could), Tentativeness (e.g. maybe, perhaps, and guess), and Certainty (e.g. always and never). These categories are abbreviated as discrep, tentat, and certain, respectively. Anxiety can be measured using the LIWC Anxiety category (anx) which includes words such as nervous, afraid, and tense. We use LIWC Future Focus (futurefocus) to measure lack of control, this category includes words such as may, will, and soon.

Social Engagement: we measure social engagement on Twitter using the average number of tweets per day.

Position in the Network: this feature can be quantified using a variety of metrics when the network structure is known. However, in the case of social networks between Twitter users, we do not have complete structure and even collecting local information is time consuming due to the rate limitation of Twitter APIs. Hence, we use the information available in our datasets and extract influence using the number of followees and popularity using the number of followers of each user.

Relationship Enhancement and Liking: improving the relation to other social media users and gaining more attention from the community is one of the motivations for spreading fake news. If the number of retweets and likes of a user’s fake news tweet is higher than the average number of the user’s retweets and likes, it can indicate that this user has enhanced their social relation and initiated conversation. Consequently, we use the difference between these two values as indicators of relationship enhancement.

The summary of features and the metrics used to measure them is presented in Figure 1. As explained in Section 2, some metrics overlap in interpreting the motivational features.

Figure 1: Summary of the metrics used to measure features of users who spread fake news with sample words used.

We study the differences between users who spread fake news and the ones who spread real news in terms of the feature categories introduced in Section 2 (RQ2). We perform two experiments towards this goal. First, we set our null hypothesis to be that these two groups have the same mean in five categories of features. If the results of t-test shows that there is a significant difference (p-value less than 0.05) between two groups, we can reject the null hypothesis. Second, we use the proposed features to see if a supervised model (a classifier) can use these features to discriminate the two groups of users. The byproduct of this experiment also validates if these features can add more information to the representation of the user’s data (RQ3).

Table 2: Comparison between fake news and real news spreaders in terms of psychological features. The features that are significantly different between users who spread fake news and the users who spread reals news are marked with ** (p-value <0.005) or * (p-value <0.05).

4.1 Significance of Psychological Features

4.2 Fake News Spreader Classifier

In the next step we examine if adding the proposed features to the representation of a text will give us a better fake news spreader classifier as our downstream task. For this section, we use Bidirectional Encoder Representations from Transformers (Bert) [10] as our model for sentence embedding. Each Twitter user’s timeline data were represented by a vector $HB$ by Bert and their motivational factors were represented by a vector $Hf$. We examined the performance of Bert as our baseline classification model by only using $HB$. For the Bert+Features model the vectors are concatenated together, $H = operatorname{Concat}(HB, Hf)$, and were passed through a feed-forward neural network to balance the effect of the two vectors [19]. The performance of the two models is presented in Table 3. This experiment shows that by adding the proposed features to context representation, we can add more value to the representation of the data for classifying fake news versus real news spreaders. We give an illustrative visualization of the embeddings of 1000 random samples of Politifact and GossipCop Datasets by both Bert and Bert+Features with the t-SNE tool [47] (Figure 2). We observe that Bert+Features can learn more discriminative embeddings.

Figure 2: The t-sne visualization of features for the Bert ((a)- and (b)-left) and Bert+Features ((a)- and (b)-right). Adding the motivational features will create a better discriminative embeddings.

Fake news detection has gained increased research attention in recent years. Fake news detection methods generally focus on using news content or social context features [42]. In news content-based approaches, features are extracted linguistically or visually. Linguistic-based features capture textual information that appears in fake news content [36], such as lexical and syntactic features. Visual-based features are used to identify fake images [15] that are intentionally created. For social context-based approaches, the features include user-based, post-based, and network-based. User-based features are extracted from user profiles [43] to measure their characteristics and credibility [7]. Post-based features represent users’ social responses in terms of stance [18], topics [26], or credibility [7]. Network-based features are extracted by constructing specific networks, such as diffusion networks [21].

Recent advancements utilize deep learning approaches to learn representations from the text, image, and network data for fake news detection. For example, convolution neural networks have been utilized to learn textual information from news content [50], and recurrent neural networks have been adopted to learn temporal representations from the propagation of fake news [24, 38]. Shu et al. utilize a network embedding approach to model the relations of the publisher, news, and users to detect fake news [44]. Deep geographic models such as graph convolution neural networks have been exploited to model the propagation networks as well as to detect fake news [31].

On the other hand, the task of profiling users has long been studied in both formal and informal languages [2, 39]. Several explicit (from metadata) and implicit cues can be used for user profiling such as user identity linkage, age, and gender [43]. Moreover, in recent research, factors reported from disciplines such as psychology and behavioral studies helped in profiling users as well. For instance, Giachanou et al. [13] combined emotional signals with a Long Short Term Memory (LSTM) neural network to detect credible versus non-credible claims. Furthermore, Cardaioli et al. [6] utilized behavioral-based features such as Big Five personality and stylometric features for discriminating a fake news spreader.

In this work, we investigated whether the psychological features that are observed in users who spread fake news in behavioral studies on human subjects are also visible in social media users who spread fake news on social media. Towards this goal, we introduced five categories of features based on psychological theories that can be quantified for social media users. Based on our observations on two real-world datasets, we observed that (i) social media users who spread fake news are significantly different in terms of the majority of these features and (ii) these features have predictive power in the detection new and unobserved fake news spreaders. This study is a first step towards understanding users who are exposed to fake news. We wish to continue this work by looking into what other psychological features associated with individual differences such as personality traits may be related to fake news susceptibility [22] as well as providing stronger proxy for each feature. Moreover, we plan to study the application of the psychological features in improving the performance of state-of-the-art fake news detection algorithms along with providing explanations for such algorithms [32].

The authors would like to thank Dr. H. Russell Bernard (ASU) and Tyler Black (ASU) for their comments on the manuscript. This material is, in part, based upon works supported by Parallax Advanced Research Corporation (11076-ASU) and ONR (N00014-21-1-4002).

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