Following detailed presentation of the Core Conflictual Relationship Theme (CCRT), there is the objective of relevant methods for what has been described as verbalization and visualization of data. Such is also termed data mining and text mining, and knowledge discovery in data. The Correspondence Analysis methodology, also termed Geometric Data Analysis, is shown in a case study to be comprehensive and revealing. Quite innovative here is how the analysis process is structured. For both illustrative and revealing aspects of the case study here, relatively extensive dream reports are used. The dream reports are from an open source repository of dream reports, and the current study proposes a possible framework for the analysis of dream report narratives, and further, how such an analysis could be relevant within the psychotherapeutic context. This Geometric Data Analysis here confirms the validity of CCRT method.
Using language algorithms to detect fake online profiles that deceive other users
Many adult content websites incorporate social networking features. Although these are popular, they raise significant challenges, including the potential for users to “catfish”, i.e., to create fake profiles to deceive other users. This paper takes an initial step towards automated catfish detection. We explore the characteristics of the different age and gender groups, identifying a number of distinctions. Through this, we train models based on user profiles and comments, via the ground truth of specially verified profiles. Applying our models for age and gender estimation of unverified profiles, we identify 38% of profiles who are likely lying about their age, and 25% who are likely lying about their gender. We find that women have a greater propensity to catfish than men. Further, whereas women catfish select from a wide age range, men consistently lie about being younger. Our work has notable implications on operators of such online social networks, as well as users who may worry about interacting with catfishes.
Paper to appear in IEEE/ACM ASONAM 2017 https://arxiv.org/abs/1705.06530
Dr Walid Magdy, University of Edinburgh, School of Informatics.
In May and June 2017, the School of Health and Social Science at the University of Edinburgh offers a series of workshops on the use of corpus linguistics and content analysis to explore language data. Such quantitative approaches to language analysis are carried out using software and can provide in-depth insight on language use and word patterns that would be too difficult and too time-consuming to identify using qualitative methods.
Our guest editors Prof. Michael. B. Buchholz and Prof. Horst Kächele have put together a special issue on “Conversational Analysis in Psychotherapy Process Research”. The special issue has excellent contributions that were originally part of the panel at the 47th SPR International Annual Meeting in Jerusalem, Israel. The panel was extremely successful and produced fruitful discussions on positioning conversational analysis in the field of psychotherapy research.
How technology could help predict terrorist attacks
The internet has become a weapon for terrorists, who use social media and other technologies to organise, recruit and spread propaganda. So is it possible to turn technology around and use it to not only catch terrorists but predict and potentially stop terror attacks before they happen?
One thing we can do is use technology to search for patterns in the activity and language of terrorists and their supporters online. If we can spot trends that typically occur in the run up to an attack, it may be possible to automatically identify when future acts of violence are being planned. In a new study, researchers from Harvard University attempted to do just this. They used computer simulations to show how unofficial groups of online Islamic State (IS) supporters spread and grow through social networking sites and how this relates to the timing of violent attacks.
This follows research into how messages on Twitter can be classified to predict whether someone will support or oppose IS. Other researchers have used data-mining techniques on social media data to try to work out when supporters “begin to adopt pro-IS behaviour”.
My research focuses on the intersection between language and clinical psychology, including both qualitative and quantitative research approaches a) to inform the development of policy guidelines and interventions to improve provision of healthcare, and b) to explore media presentations of mental health.
I am also the co-founding editor (together with Dr. Andrew Wilson) of the journal ‘Language and Psychoanalysis’. The ‘Language and Mind Network’ which aims to bring together individuals with an interest in the intersection of language and psychology, including psychotherapy, clinical psychology and the humanities, and thus to encourage dialogue and collaboration.
Affiliation : International Psychoanalytic University Berlin
Prof. Dr. Michael B. Buchholz, Dipl.-Psych., Professor for Social Psychology at the International Psychoanalytic University (IPU), Berlin (Germany), head of the Dissertation Program at IPU. PhD in Psychoanalysis 1980 (Frankfurt), Habilitation in Social Sciences 1990 in Göttingen; Psychoanalyst and Training Analyst in the German Psychoanalytic Society. Editorial board of “System Familie”, “Psychotherapie und Sozialwissenschaft”, “Psychosozial”, “International Forum of Psychoanalysis”, „Language and Psychoanalysis“. More than 150 publications. Qualitative studies: analysis of a 30 session short-term therapy (1996), scenarios of contact (1997), sexual offenders in group therapy (2008), empathy conversations in psychotherapy.