Search results for: LIWC

Authors: Vesile Evrim, Aliyu Awwal

Abstract:

Today as in the other domains, there are an enormous number of political transcripts available in the Web which is waiting to be mined and used for various purposes such as statistics and recommendations. Therefore, automatically determining the political orientation on these transcripts becomes crucial. The methodologies used by machine learning algorithms to do the automatic classification are based on different features such as Linguistic. Considering the ideology differences between Liberals and Conservatives, in this paper, the effect of Personality Traits on political orientation classification is studied. This is done by considering the correlation between LIWC features and the BIG Five Personality Traits. Several experiments are conducted on Convote U.S. Congressional-Speech dataset with seven benchmark classification algorithms. The different methodologies are applied on selecting different feature sets that constituted by 8 to 64 varying number of features. While Neuroticism is obtained to be the most differentiating personality trait on classification of political polarity, when its top 10 representative features are combined with several classification algorithms, it outperformed the results presented in previous research.

Keywords: politics, personality traits, LIWC, machine learning

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Authors: Meaghan Bowman

Abstract:

Microblogging has become increasingly popular for commenting on current events, spreading gossip, and encouraging individualism--which favors its low-context communication channel. These social media (SM) platforms allow users to express opinions while interacting with a wide range of populations. Hashtags allow immediate identification of like-minded individuals worldwide on a vast array of topics. The output of the analytic tool, Linguistic Inquiry and Word Count (LIWC)--a program that associates psychological meaning with the frequency of use of specific words--may suggest the nature of individuals’ internal states and general sentiments. When applied to groupings of SM posts unified by a hashtag, such information can be helpful to community leaders during periods in which the forming of public opinion happens in parallel with the unfolding of political, economic, or social events. This is especially true when outcomes stand to impact the well-being of the group. Here, we applied the online tools, Google Translate and the University of Texas’s LIWC, to a 90-posting sample from a corpus of Colombian Spanish microblogs. On translated disjoint sets, identified by hashtag as being authored by advocates of voting “No,” advocates voting “Yes,” and entities refraining from hashtag use, we observed the value of LIWC’s Tone feature as distinguishing among the categories and the word “peace,” as carrying particular significance, due to its frequency of use in the data.

Keywords: Colombia peace referendum, FARC, hashtags, linguistics, microblogging, social media

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Authors: Vesile Evrim, Aliyu Awwal

Abstract:

By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Charlotte Entwistle, Ryan Boyd

Abstract:

Recent developments in information retrieval techniques and natural language processing have allowed for greater exploration of psychological and social processes. Linguistic analysis methods for understanding behaviour have provided useful insights within the field of mental health. One area within mental health that has received little attention though, is borderline personality disorder (BPD). BPD is a common mental health disorder characterised by instability of interpersonal relationships, self-image and affect. It also manifests through maladaptive behaviours, such as impulsivity and self-harm. Examination of language patterns associated with BPD could allow for a greater understanding of the disorder and its links to maladaptive thoughts and behaviours. Language analysis methods could also be used in a predictive way, such as by identifying indicators of BPD or predicting maladaptive thoughts, emotions and behaviours. Additionally, associations that are uncovered between language and maladaptive thoughts and behaviours could then be applied at a more general level. This study explores linguistic characteristics of BPD, and their links to maladaptive thoughts and behaviours, through the analysis of social media data. Data were collected from a large corpus of posts from the publicly available social media platform Reddit, namely, from the ‘r/BPD’ subreddit whereby people identify as having BPD. Data were collected using the Python Reddit API Wrapper and included all users which had posted within the BPD subreddit. All posts were manually inspected to ensure that they were not posted by someone who clearly did not have BPD, such as people posting about a loved one with BPD. These users were then tracked across all other subreddits of which they had posted in and data from these subreddits were also collected. Additionally, data were collected from a random control group of Reddit users. Disorder-relevant behaviours, such as self-harming or aggression-related behaviours, outlined within Reddit posts were coded to by expert raters. All posts and comments were aggregated by user and split by subreddit. Language data were then analysed using the Linguistic Inquiry and Word Count (LIWC) 2015 software. LIWC is a text analysis program that identifies and categorises words based on linguistic and paralinguistic dimensions, psychological constructs and personal concern categories. Statistical analyses of linguistic features could then be conducted. Findings revealed distinct linguistic features associated with BPD, based on Reddit posts, which differentiated these users from a control group. Language patterns were also found to be associated with the occurrence of maladaptive thoughts and behaviours. Thus, this study demonstrates that there are indeed linguistic markers of BPD present on social media. It also implies that language could be predictive of maladaptive thoughts and behaviours associated with BPD. These findings are of importance as they suggest potential for clinical interventions to be provided based on the language of people with BPD to try to reduce the likelihood of maladaptive thoughts and behaviours occurring. For example, by social media tracking or engaging people with BPD in expressive writing therapy. Overall, this study has provided a greater understanding of the disorder and how it manifests through language and behaviour.

Keywords: behaviour analysis, borderline personality disorder, natural language processing, social media data

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Authors: A. Sainvil, J. Mallela, L. M. Pereira

Abstract:

Adolescents and young adults (AYA) diagnosed with cancer are tasked with managing their health through treatment, a time when reliance on and independence from parents may change in unexpected ways. Resilience allows patients to cope and manage their own health through treatment, promoting motivation and a healthier lifestyle. The film acts as a source of reflection through the cancer journey, which may have an impact on how patients cope. The current research investigated relationships between resilient linguistic qualities of the video narratives and attitudes toward personal health management. N=24 patients diagnosed between ages 11-18 were recruited. First, participants provided demographic information, then made a video testimonial about their cancer experience. After filming, participants then completed a questionnaire on the perceived benefits for themselves and others for making the video. Videos were transcribed and analyzed for thematic content via codebook and for linguistic qualities, indicating resilience with the use of the Linguistic Inquiry and Word Count Analysis Program (LIWC). Linear regressions were then calculated to explore relationships between resilient qualities, thematic content, and participants’ perceptions of their medical team and willingness to care for themselves. Participants who spoke with greater narrator connectedness were more likely to change their view of their medical team (β=.628 p=.034). When a participant believed that providers were likely to view their video, they were marginally more likely to want to take better care of themselves (β=.367, p=.078). Participants who spoke in depth about their health reported higher intention to take better care of themselves (β=.785, p=.033). AYAs with cancer who showcased certain resilient qualities within their narrative were more likely to consider taking better care of themselves. Additionally, the more patients reflected on their health, the more they wanted to take better care of themselves. These relationships were stronger when a patient believed that a provider would watch their video. Study findings highlight the utility of film in uncovering aspects of resilience and coping that may lead to healthier behaviors in AYAs with cancer.

Keywords: adolescents, cancer, resilience, health management

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Authors: Selina Studer, Maria Kleinstäuber, Cornelia Weise

Abstract:

Background: Finding words to report what you have been through may be challenging, especially when dealing with stressful or highly emotional experiences. Photovoice (PV) represents a possible way of facilitating experience reporting. In this approach, people take photos about a particular topic (in our study: worries about the future) and talk about the topic based on the photos. So far, the benefits of Photovoice have been quantitatively insufficiently tested. There is a lack of randomized controlled trials investigating PV in comparison to other methods. This study aimed to fill this research gap. Methods: 65 participants took part in the study and were randomly assigned to the PV group, the writing group (WG), or the control group (CG). The PV group received the task to take photos of worries regarding the future for one week and send max. 5 of them to the interviewer before the interview. The WG had to write down the worries about the future and send max. 5 of them to the interviewer before the interview. The control group did not receive a specific assignment. The semi-structured interview consisted of six open-ended questions and was applied to all future worries. The questions included the content of the future worries, the meaning, and how the worry expressed itself emotionally and physically. The interview was recorded and later transcribed. After the interview, online questionnaires were filled out. They covered a range of variables such as access to emotional content, ability to describe feelings, the extent of self-disclosure, and relationship quality. Results: Contrary to our hypotheses, one-way ANOVA revealed no differences between the three conditions concerning all variables (access to emotional content, ability to describe feelings, the extent of self-disclosure, and so on), all p's > 0.14, BF₀₁ = 1.78-7.66. In a subsequent step, the words in the transcribed interviews were analyzed. The LIWC program counted how many emotional words occurred in the text and assigned them to predefined categories. Planned contrasts revealed that the PV reported more negative emotional words compared to the two groups t(62) = 2.62, p = .011, and also compared to the WG only, t(62) = 2.36, p = .022, BF₀₁ = 0.62. Conclusions and implications: The applied self-report instruments did not reveal any differences between the groups. However, the PV group used more negative emotional words than the other two groups. The discrepancy between self-report and observation variables regarding emotionality is noticeable. It is suggested that the highly educated and above-average female sample may not have needed PV to access emotional content. It is possible that the approach would yield clearer results in a clinical sample. This and other approaches are currently being investigated in a follow-up study.

Keywords: photovoice, controlled randomized study, online intervention, emotional awareness, self-disclosure, data triangulation, interviews

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