Teaching the Computer to Code Frames in News : Comparing Two Supervised Machine Learning Approaches to Frame Analysis

We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation of framing processes conceptually as well as methodologically. We conduct several experiments in which we automate the coding of four generic frames that are operationalised as a set of indicator questions. In doing so, we compare two approaches to modelling the coherence between indicator questions and frames as an SML task. The results of our experiments show that SML is well suited to automate frame coding but that coding performance is dependent on the way SML is implemented

A frameshift polymorphism in P2X5 elicits an allogeneic cytotoxic T lymphocyte response associated with remission of chronic myeloid leukemia

Minor histocompatibility antigens (mHAgs) constitute the targets of the graft-versus-leukemia response after HLA-identical allogeneic stem cell transplantation. Here, we have used genetic linkage analysis to identify a novel mHAg, designated lymphoid-restricted histocompatibility antigen–1 (LRH-1), which is encoded by the P2X5 gene and elicited an allogeneic CTL response in a patient with chronic myeloid leukemia after donor lymphocyte infusion. We demonstrate that immunogenicity for LRH-1 is due to differential protein expression in recipient and donor cells as a consequence of a homozygous frameshift polymorphism in the donor. Tetramer analysis showed that emergence of LRH-1–specific CD8(+) cytotoxic T cells in peripheral blood and bone marrow correlated with complete remission of chronic myeloid leukemia. Furthermore, the restricted expression of LRH-1 in hematopoietic cells including leukemic CD34(+) progenitor cells provides evidence of a role for LRH-1–specific CD8(+) cytotoxic T cells in selective graft-versus-leukemia reactivity in the absence of severe graft-versus-host disease. These findings illustrate that the P2X5-encoded mHAg LRH-1 could be an attractive target for specific immunotherapy to treat hematological malignancies recurring after allogeneic stem cell transplantation

Teaching the Computer to Code Frames in News: Comparing Two Supervised Machine Learning Approaches to Frame Analysis

We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation of framing processes conceptually as well as methodologically. We conduct several experiments in which we automate the coding of four generic frames that are operationalised as a set of indicator questions. In doing so, we compare two approaches to modelling the coherence between indicator questions and frames as an SML task. The results of our experiments show that SML is well suited to automate frame coding but that coding performance is dependent on the way SML is implemented